CONSTRUÇÃO DE BANCOS DE FACES PARA TAREFAS DE RECONHECIMENTO DE EXPRESSÕES FACIAIS DE EMOÇÕES BÁSICAS: UMA REVISÃO SISTEMÁTICA

Fabrício, Daiene de Morais; Ferreira, Bianca Letícia Cavalmoretti; Maximiano-Barreto, Madson Alan; Muniz, Monalisa; Chagas, Marcos Hortes Nisihara

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Brasil

Dementia & Neuropsychologia

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Brasil

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Sumário

Views & Reviews • Dement. neuropsychol. 16 (4) • Dez 2022 • https://doi.org/10.1590/1980-5764-dn-2022-0039 copiar

CONSTRUÇÃO DE BANCOS DE FACES PARA TAREFAS DE RECONHECIMENTO DE EXPRESSÕES FACIAIS DE EMOÇÕES BÁSICAS: UMA REVISÃO SISTEMÁTICA

Autoria SCIMAGO INSTITUTIONS RANKINGS

RESUMO.

Reconhecer as emoções do outro é uma habilidade importante para o contexto social, que pode ser modulada por variáveis como sexo, idade e raça. Vários estudos buscam elaborar bancos de faces específicos para avaliar o reconhecimento de emoções básicas em diferentes contextos.

Objetivos:

Esta revisão sistemática buscou reunir esses estudos, descrevendo e comparando as metodologias utilizadas em sua elaboração.

Métodos:

As bases de dados utilizadas para a seleção dos artigos foram: PubMed, Web of Science, PsycInfo e Scopus. Foi utilizado o seguinte cruzamento de palavras: “facial expression database OR stimulus set AND development OR validation”.

Resultados:

O total de 36 artigos mostrou que a maioria dos estudos utilizou atores para expressar as emoções, que foram suscitadas de situações específicas para serem o mais espontâneas possível. Os bancos de faces foram compostos principalmente de estímulos coloridos e estáticos. Além disso, a maioria dos estudos buscou estabelecer e descrever padrões para registrar os estímulos, como a cor das roupas utilizadas e o fundo. As propriedades psicométricas dos bancos de faces também são descritas.

Conclusões:

Os dados apresentados nesta revisão apontam para a heterogeneidade metodológica entre os estudos. Apesar disso, descrevemos seus padrões, contribuindo para o planejamento de novas pesquisas que buscam criar bancos de faces específicos para novos contextos.

Palavras-chave:
Expressão Facial; Estudo de Validação; Emoções; Reconhecimento Facial; Psicometria

Authors and year of publication	Study location	Name of the built database	Theoretical reference	Characteristics of participants who expressed emotions	Basic emotions expressed	Total of stimuli	Specific characteristics of stimuli
Benda and Scherf. (2020)²⁵25 Benda MS, Scherf KS. The complex emotion expression database: a validated stimulus set of trained actors. PLoS One. 2020;15(2):e0228248. https://doi.org/10.1371/journal.pone.0228248 https://doi.org/10.1371/journal.pone.022...	The United States	Complex Emotion Expression Database (CEED)	Recognition of complex emotions in young people (Empirical)	8 professional actors – Age: 20.9 years; SD=3.1 – Sex: M=4; F=4 – Race: Caucasians (n=4) and Black (n=4)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise	243 images	– Black and white – Static
Chung et al. (2019)²⁶26 Chung KM, Kim S, Jung WH, Kim Y. Development and validation of the Yonsei face database (YFace DB). Front Psychol. 2019;10:2626. https://doi.org/10.3389/fpsyg.2019.02626 https://doi.org/10.3389/fpsyg.2019.02626...	South Korea	Yonsei Face Database (YFace DB)	Basic emotions (Ekman and Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975.	74 local community and university volunteers – Age: 19-40 years – Sex: M=37; F=37 – Race: Koreans	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	1,480 stimuli	– Colorful – Static and dynamic ^* * Additional features of the face database. Open and closed mouth ^* * Additional features of the face database. Varied intensities
Conley et al. (2018)¹⁶16 Conley MI, Dellarco DV, Rubien-Thomas E, Cohen AO, Cervera A, Tottenham N, et al. The racially diverse affective expression (RADIATE) face stimulus set. Psychiatry Res. 2018;270:1059-67. https://doi.org/10.1016/j.psychres.2018.04.066 https://doi.org/10.1016/j.psychres.2018....	The United States	The racially diverse affective expression (RADIATE)	Racial heterogeneity in emotion recognition (Empirical)	109 community adults – Age: 18-30 years – Sex: M=53; F=56 – Race: Asian (n=22), Black/African-Americans (n=38), Caucasians (n=28), Hispanic (n=20) and others (n=1)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	1,721 images	– Colorful and black and white – Static ^* * Additional features of the face database. Open and closed mouth
Dalrymple et al. (2013)²⁷27 Dalrymple KA, Gomez J, Duchaine B. The dartmouth database of children's faces: acquisition and validation of a new face stimulus set. PLoS One. 2013;8(11):e79131. https://doi.org/10.1371/journal.pone.0079131 https://doi.org/10.1371/journal.pone.007...	The United States	The Dartmouth Database of Children's Faces	Recognition of emotions in children (Empirical)	80 community children – Age: 9.84 years; SD=2.33 – Sex: M=40; F=40 – Race: Caucasians	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	964 images	– Colorful – Static ^* * Additional features of the face database. Happiness with closed mouth and happiness showing teeth
Donadon et al. (2019)²⁸28 Donadon MF, Martin-Santos R, Osório FL. Baby faces: development and psychometric study of a stimuli set based on babies’ emotions. J Neurosci Methods. 2019;311:178-85. https://doi.org/10.1016/j.jneumeth.2018.10.021 https://doi.org/10.1016/j.jneumeth.2018....	Brazil	Baby Faces	Ekman's Neurocultural Theory (1972)⁵⁷57 Ekman P. Universals and cultural differences in facial expressions of emotion. In J. Cole (Ed.). Nebraska Symposium on Motivation. Lincoln: University of Nebraska Press; 1972. p. 207-82.	20 babies – Age: 9 months; SD=1.5 – Sex: M=10; F=10 – Race: Caucasians (n=66%), Black (n=17%), and Japanese (n=17%)	1) Happiness 2) Sadness 3) Fear 4) Anger 5) Surprise 6) Neutral	57 images	– Colorful – Static
Ebner et al. (2010)¹³13 Ebner NC, Riediger M, Lindenberger U. FACES--a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav Res Methods. 2010;42(1):351-62. https://doi.org/10.3758/BRM.42.1.351 https://doi.org/10.3758/BRM.42.1.351...	Germany	Faces--a life-span Database of Facial Expressions	Age differences in emotion recognition (Ruffman et al., 2008)⁵⁸58 Ruffman T, Henry JD, Livingstone V, Phillips LH. A meta-analytic review of emotion recognition and aging: implications for neuropsychological models of aging. Neurosci Biobehav Rev. 2008;32(4):863-81. https://doi.org/10.1016/j.neubiorev.2008.01.001 https://doi.org/10.1016/j.neubiorev.2008...	179 actors and extras recruited from a modeling agency – 61 young (24.3 years; SD=3.5) – 60 middle-age (49.0 years; SD=3.9) – 58 elderly (73.2 years; SD=2.8) – Sex: M=86; F=85 – Race: Caucasians (n=179)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Neutral	2,052 images	– Colorful – Static
Egger et al. (2011)²⁹29 Egger HL, Pine DS, Nelson E, Leibenluft E, Ernst M, Towbin KE, et al. The NIMH Child Emotional Faces Picture Set (NIMH-ChEFS): a new set of children's facial emotion stimuli. Int J Methods Psychiatr Res. 2011;20(3):145-56. https://doi.org/10.1002/mpr.343 https://doi.org/10.1002/mpr.343...	The United States	NIMH Child Emotional Faces Picture Set (NIMH-ChEFS)	Recognition of emotions in children (Empirical)	59 child actors – Age: 13.6 years – Sex: M=20; F=39 – Race: ND	1) Happiness 2) Sadness 3) Fear 4) Anger 5) Neutral	482 images	– Colorful – Static ^* * Additional features of the face database. Two directions of gazing: direct and avoided
Ekman and Friesen. (1976)³⁰30 Ekman P, Friesen WV. Pictures of facial affect. Palo Alto: Consulting Psychologists Press; 1976.	The United States	Pictures of Facial Affect (POFA)	Pan-cultural elements in facial expressions of emotions (Ekman et al., 1969)⁵5 Ekman P, Sorenson ER, Friesen WV. Pan-cultural elements in facial displays of emotion. Science. 1969;164(3875):86-8. https://doi.org/10.1126/science.164.3875.86 https://doi.org/10.1126/science.164.3875...	10 individuals – Age: ND – Sex: M=4; F=6 – Race: Caucasians and African-American	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	110 images	– Black and white – Static
Fujimura and Umemura. (2018)³¹31 Fujimura T, Umemura H. Development and validation of a facial expression database based on the dimensional and categorical model of emotions. Cogn Emot. 2018;32(8):1663-70. https://doi.org/10.1080/02699931.2017.1419936 https://doi.org/10.1080/02699931.2017.14...	Japan	A facial expression database based on the dimensional and categorical model of emotions	The influence of angles on emotion recognition (Borod et al., 1998)⁵⁹59 Borod JC, Koff E, Yecker S, Santschi C, Schmidt JM. Facial asymmetry during emotional expression: gender, valence, and measurement technique. Neuropsychologia. 1998;36(11):1209-15. https://doi.org/10.1016/s0028-3932(97)00166-8. https://doi.org/10.1016/s0028-3932(97)00...	8 professional actors – Age: 34.25 years; SD=5.47 – Sex: M=4; F=4 – Race: Japanese	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	920 stimuli	– Colorful – Static and dynamic ^* * Additional features of the face database. Open and closed mouth ^* * Additional features of the face database. Varied angles
Franz et al. (2021)³²32 Franz M, Müller T, Hahn S, Lundqvist D, Rampoldt D, Westermann JF, et al. Creation and validation of the Picture-Set of Young Children's Affective Facial Expressions (PSYCAFE). PLoS One. 2021;16(12):e0260871. https://doi.org/10.1371/journal.pone.0260871 https://doi.org/10.1371/journal.pone.026...	Germany	Picture-Set of Young Children's Affective Facial Expressions (PSYCAFE)	Recognition of emotions in children (Empirical)	35 children – Age: 4-6 years – Sex: M=14; F=21 – Race: ND	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	104 images	– Colorful – Static ^* * Additional features of the face database. Varied intensities
Garrido et al. (2017)³³33 Garrido MV, Lopes D, Prada M, Rodrigues D, Jerónimo R, Mourão RP. The many faces of a face: comparing stills and videos of facial expressions in eight dimensions (SAVE database). Behav Res Methods. 2017;49(4):1343-60. https://doi.org/10.3758/s13428-016-0790-5 https://doi.org/10.3758/s13428-016-0790-...	Portugal	Stills and Videos of facial Expressions (SAVE database)	Recognition of emotions in dynamic stimuli (Empirical)	20 students – Age: 21.75 years; SD=1.97 – Sex: M=12; F=8 – Race: ND	1) Happiness 2) Neutral	120 stimuli	– Colorful – Static and dynamic
Giuliani et al. (2017)¹⁵15 Giuliani NR, Flournoy JC, Ivie EJ, Von Hippel A, Pfeifer JH. Presentation and validation of the DuckEES child and adolescent dynamic facial expressions stimulus set. Int J Methods Psychiatr Res. 2017;26(1):e1553. https://doi.org/10.1002/mpr.1553 https://doi.org/10.1002/mpr.1553...	The United States	The DuckEES child and adolescent dynamic facial expressions stimulus set	Recognition of emotions in dynamic stimuli (Empirical)	37 children and teenage actors – Age: 13.24 years; SD=2.09 – Sex: M=15; F=22 – Race: Caucasians (n=89%)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Neutral	120 videos	– Colorful – Dynamic
Happy et al. (2015)³⁴34 Happy SL, Patnaik P, Routray A, Guha R. The Indian spontaneous expression database for emotion recognition. IEEE Transactions on Affective Computing. 2015;8(1):131-42. https://doi.org/10.1109/TAFFC.2015.2498174 https://doi.org/10.1109/TAFFC.2015.24981...	India	The Indian Spontaneous Expression Database for Emotion Recognition (ISED)	Basic emotions (Ekman and Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975.	50 individuals – Age: 18-22 years – Sex: M=29; F=21 – Race: Indians	1) Happiness 2) Sadness 3) Disgust 4) Surprise	428 videos	– Colorful – Dynamic ^* * Additional features of the face database. Varied intensities
Kaulard et al. (2012)³⁵35 Kaulard K, Cunningham DW, Bülthoff HH, Wallraven C. The MPI facial expression database--a validated database of emotional and conversational facial expressions. PLoS One. 2012;7(3):e32321. https://doi.org/10.1371/journal.pone.0032321 https://doi.org/10.1371/journal.pone.003...	Germany	The MPI Facial Expression Database	Language and emotions (Empirical)	19 native Germans without professional acting experience – Age: 20-30 years – Sex: M=9; F=10 – Race: Caucasians	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger	18800 videos	– Colorful – Dynamic ^* * Additional features of the face database. Varied angles
Keutmann et al. (2015)³⁶36 Keutmann MK, Moore SL, Savitt A, Gur RC. Generating an item pool for translational social cognition research: methodology and initial validation. Behav Res Methods. 2015;47(1):228-34. https://doi.org/10.3758/s13428-014-0464-0 https://doi.org/10.3758/s13428-014-0464-...	The United States	Visual and vocal emotional expressions of adult and child actors	Item Response Theory in face database construction (Empirical)	150 actors (Adults: n=139 and kids: n=11) – Age: 36.1 years; SD=15.6 – Sex: M=73; F=77 – Race: Caucasians (n=98), African-American (n=35), Hawaiian (n=1), mixed (n=1), and others (n=1)	1) Happiness 2) Sadness 3) Fear 4) Anger 5) Neutral	152 stimuli	– Colorful – Static and dynamic ^* * Additional features of the face database. Varied intensities
Kim et al. (2017)³⁷37 Kim SM, Kwon YJ, Jung SY, Kim MJ, Cho YS, Kim HT, et al. Development of the Korean facial emotion stimuli: Korea university facial expression collection 2nd edition. Front Psychol. 2017;8:769. https://doi.org/10.3389/fpsyg.2017.00769 https://doi.org/10.3389/fpsyg.2017.00769...	South Korea	Korea University Facial Expression Collection – Second Edition (KUFEC-II)	The role of culture in recognizing emotions (Empirical)	57 actors – Age: ND – Sex: M=32; F=36 – Race: Koreans	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	399 images	– Colorful – Static
Langner et al. (2010)³⁸38 Langner O, Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, van Knippenberg A. Presentation and validation of the Radboud Faces Database. Cognition and Emotion. 2010;24(8):1377-88. https://doi.org/10.1080/02699930903485076 https://doi.org/10.1080/0269993090348507...	Netherlands	Radboud Faces Database	The influence of angles and direction of gaze on emotion recognition (Empirical)	49 young and children Young: 39 Children: 10 – Age: ND – Sex: M=24; M=25 – Race: Caucasians (n=49)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	5,880 images	– Colorful – Static ^* * Additional features of the face database. Three directions of gaze: front, right, and left ^* * Additional features of the face database. Varied face angles
LoBue and Thrasher. (2015)¹⁴14 LoBue V, Thrasher C. The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults. Front Psychol. 2015;5:1532. https://doi.org/10.3389/fpsyg.2014.01532 https://doi.org/10.3389/fpsyg.2014.01532...	The United States	The Child Affective Facial Expression (CAFE)	Recognition of emotions in children's faces of different races (Empirical)	154 children – Age: 5.3 years – Sex: M=64; F=90 – Race: African-Americans (n=27), Caucasians (n=77), Asians (n=16), Latinos (n=23), and South Asia (n=11)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	1,192 images	– Colorful – Static ^* * Additional features of the face database. Open and closed mouth
Lundqvist et al. (1998)³⁹39 Lundqvist D, Flykt A, Öhman A. The Karolinska directed emotional faces--KDEF. (CD ROM). Stockholm: Karolinska Institute, Department of Clinical Neuroscience, Psychology Section; 1998.	Sweden	Karolinska Directed Emotional Faces (KDEF) Database	-	70 actors – Age: 25 years (20-30 years) – Sex: M=35; F=35 – Race: ND	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	490 images	– Colorful – Static ^* * Additional features of the face database. Varied face angles
Ma et al. (2020)⁴⁰40 Ma J, Yang B, Luo R, Ding X. Development of a facial-expression database of Chinese Han, Hui and Tibetan people. Int J Psychol. 2020;55(3):456-64. https://doi.org/10.1002/ijop.12602 https://doi.org/10.1002/ijop.12602...	China	Han, Hui, and Tibetan Chinese facial expression database	The role of culture in recognizing emotions (Empirical)	630 volunteers – Age: Han (22 years; SD=2.7); Hui (22.8 years; SD=2.4); and Tibet (21.4 years; SD=2.5) – Sex: M=315; F=315 – Race: Chinese from different regions	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	930 images	– Colorful – Static
Ma et al. (2015)⁴¹41 Ma DS, Correll J, Wittenbrink B. The Chicago face database: a free stimulus set of faces and norming data. Behav Res Methods. 2015;47(4):1122-35. https://doi.org/10.3758/s13428-014-0532-5 https://doi.org/10.3758/s13428-014-0532-...	The United States	Chicago Face Database (CFD)	Limitations of existing face databases (Empirical)	158 individuals from the University of Chicago Laboratory and amateur actors – Age: 13.6 years – Sex: M=73; F=85 – Race: Black (n=85) and Caucasians (n=73)	1) Happiness 2) Fear 3) Neutral	158 images	– Colorful – Static ^* * Additional features of the face database. Two directions of gaze: direct and averted
Maack et al. (2017)⁴²42 Maack JK, Bohne A, Nordahl D, Livsdatter L, Lindahl ÅAW, Øvervoll M, et al. The Tromso Infant Faces Database (TIF): development, validation and application to assess parenting experience on clarity and intensity ratings. Front Psychol. 2017;8:409. https://doi.org/10.3389/fpsyg.2017.00409 https://doi.org/10.3389/fpsyg.2017.00409...	Norway	The Tromso Infant Faces Database (TIF)	Influence of child stimuli on the adult attention system (Brosch et al., 2007; Parsons et al., 2011; Borgi et al., 2014)^60-62	18 babies – Age: 4-12 months – Sex: M=8; F=10 – Race: Caucasians	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	119 images	– Colorful – Static
Meuwissen et al. (2017)⁴³43 Meuwissen AS, Anderson JE, Zelazo PD. The creation and validation of the developmental emotional faces stimulus set. Behav Res Methods. 2017;49(3):960-6. https://doi.org/10.3758/s13428-016-0756-7 https://doi.org/10.3758/s13428-016-0756-...	The United States	Developmental Emotional Faces Stimulus Set (DEFSS)	Limitations of existing face databases (Empirical)	116 volunteers 42 children 44 teenagers 30 adults – Age: ND – Sex: M=43; F=73 – Race: White (n=102), non-White (n=15)	1) Happiness 2) Sadness 3) Fear 4) Anger 5) Neutral	404 images	– Colorful – Static
Minear and Park. (2004)⁴⁴44 Minear M, Park DC. A lifespan database of adult facial stimuli. Behav Res Methods Instrum Comput. 2004;36(4):630-3. https://doi.org/10.3758/bf03206543 https://doi.org/10.3758/bf03206543...	The United States	A lifespan database of adult facial stimuli	Influence of age on emotion recognition (Empirical)	576 community volunteers – Age: 18-93 years – Sex: M=219; F=357 – Race: Caucasians (n=435), African-American (n=89), and others (n=52)	1) Happiness 2) Neutral	1,142 images	– Colorful – Static
Negrão et al. (2021)⁴⁵45 Negrão JG, Osorio AAC, Siciliano RF, Lederman VRG, Kozasa EH, D'Antino MEF, et al. The child emotion facial expression set: a database for emotion recognition in children. Front Psychol. 2021;12:666245. https://doi.org/10.3389/fpsyg.2021.666245 https://doi.org/10.3389/fpsyg.2021.66624...	Brazil	The Child Emotion Facial Expression Set	Recognition of emotions in children (Empirical)	132 children – Age: 4-6 years – Sex: M=42%; F=58% – Race: Caucasian (n=71%), African (n=24%), Asian (5%)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	971 stimuli	– Colorful – Static and dynamic
Novello et al. (2018)⁴⁶46 Novello B, Renner A, Maurer G, Musse S, Arteche A. Development of the youth emotion picture set. Perception. 2018;47(10-11):1029-42. https://doi.org/10.1177/0301006618797226 https://doi.org/10.1177/0301006618797226...	Brazil	Youth Emotion Picture Set	Recognition of Facial Emotions in Teens (Empirical)	31 randomly selected volunteers – Age: 17.4 years; SD=2.7 – Sex: M=14; F=17 – Race: Caucasians (n=27), Blacks (n=1), and mixed (n=3)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	42 images	– Black and white – Static
O'Reilly et al. (2016)⁴⁷47 O'Reilly H, Pigat D, Fridenson S, Berggren S, Tal S, Golan O, et al. The EU-emotion stimulus set: a validation study. Behav Res Methods. 2016;48(2):567-76. https://doi.org/10.3758/s13428-015-0601-4 https://doi.org/10.3758/s13428-015-0601-...	The United Kingdom	The EU-Emotion Stimulus Set	Limitations of existing face databases (Empirical)	19 actors – Age: 10-70 years – Sex: M=9; F=10 – Race: Caucasians (n=13), Afro-Caribbean/British-Asian (n=2), Blacks (n=2), mixed white/Asian (n=1), Mediterranean/Asian-British (n=1)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	249 videos	– Colorful – Dynamic
Olszanowski et al. (2015)⁴⁸48 Olszanowski M, Pochwatko G, Kuklinski K, Scibor-Rylski M, Lewinski P, Ohme RK. Warsaw set of emotional facial expression pictures: a validation study of facial display photographs. Front Psychol. 2015;5:1516. https://doi.org/10.3389/fpsyg.2014.01516 https://doi.org/10.3389/fpsyg.2014.01516...	Poland	Warsaw set of emotional facial expression. pictures (WSEFEP)	Limitations of existing face databases (Empirical)	30 professional actors – Age: 20-30 years – Sex: M=14; F=16 – Race: Polish	1) Happiness 2) Sadness 3) Fear 4) Anger 5) Surprise 6) Neutral	210 images	– Colorful – Static
Passareli et al. (2018)⁴⁹49 Passarelli M, Masini M, Bracco F, Petrosino M, Chiorri C. Development and validation of the Facial Expression Recognition Test (FERT). Psychol Assess. 2018;30(11):1479-90. https://doi.org/10.1037/pas0000595 https://doi.org/10.1037/pas0000595...	Italy	Facial Expression Recognition Test (FERT)	Basic emotions (Ekman e Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975. and Item Response Theory (Reise and Revicki, 2014)⁶³63 Reise SP, Revicki DA. Handbook of item response theory modeling. New York: Taylor & Francis; 2014.	6 professional actors – Age: ND – Sex: M=3; F=3 – Race: ND	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	42 images	– Colorful – Static
Romani-Sponchiado et al. (2015)⁵⁰50 Romani-Sponchiado A, Sanvicente-Vieira B, Mottin C, Hertzog-Fonini D, Arteche A. Child Emotions Picture Set (CEPS): development of a database of children's emotional expressions. Psychology & Neuroscience. 2015;8(4):467-78. https://doi.org/10.1037/h0101430 https://doi.org/10.1037/h0101430...	Brazil	Child Emotions Picture Set	Recognition of facial emotions in children (Empirical)	18 children – Age: 6-7 years (6.93 years; SD=0.3); 8-9 years (9.12 years; SD=0.57), and 10-11 years (10.72 years; SD=0.61) – Sex: M=9; F=9 – Race: Caucasians (n=14), African-American (n=3), and Indigenous (n=1)	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	225 images	– Black and white – Static ^* * Additional features of the face database. Varied intensities
Samuelsson et al. (2012)⁵¹51 Samuelsson H, Jarnvik K, Henningsson H, Andersson J, Carlbring P. The Umeå university database of facial expressions: a validation study. J Med Internet Res. 2012;14(5):e136. https://doi.org/10.2196/jmir.2196 https://doi.org/10.2196/jmir.2196...	Sweden	Umeå University Database of Facial Expressions	Limitations of existing face databases (Empirical)	60 community individuals – Age: 17-67 years (30.19 years; SD=10.66) – Sex: M=30; F=30 – Race: Swedes, Central Europe, Arabs, and Asians	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	424 images	– Colorful – Static
Sharma and Bhushan. (2019)⁵²52 Sharma U, Bhushan B. Development and validation of Indian Affective Picture Database. Int J Psychol. 2019;54(4):462-7. https://doi.org/10.1002/ijop.12471 https://doi.org/10.1002/ijop.12471...	India	Indian Affective Picture	Basic emotions (Ekman and Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975. and limitations of existing face databases (Empirical)	4 professional actors – Age: 25.25 years; SD=3.77 – Sex: M=2; F=2 – Race: Indians	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	140 images	– Colorful – Static ^* * Additional features of the face database. Varied face angles
Tottenham et al. (2009)¹²12 Tottenham N, Tanaka JW, Leon AC, McCarry T, Nurse M, Hare TA, et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 2009;168(3):242-9. https://doi.org/10.1016/j.psychres.2008.05.006 https://doi.org/10.1016/j.psychres.2008....	The United States	The NimStim set of facial expressions	Basic emotions (Ekman and Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975. and limitations of existing face databases (Empirical)	43 professional actors – Age: 21-30 years – Sex: M=25; F=18 – Race: Africans, Europeans, and Latin Americans	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	672 images	– Colorful – Static ^* * Additional features of the face database. Open and closed mouth
Tracy et al. (2009)⁵³53 Tracy JL, Robins RW, Schriber RA. Development of a FACS-verified set of basic and self-conscious emotion expressions. Emotion. 2009;9(4):554-9. https://doi.org/10.1037/a0015766 https://doi.org/10.1037/a0015766...	Canada	Universidade da Califórnia, Davis, Set of Emotion Expressions (UCDS)	Basic emotions (Ekman and Friesen, 1975)⁵⁶56 Ekman P, Friesen WV. Unmasking the face: a guide to recognizing emotions from facial clues. Nova Jersey: Prentice-Hall; 1975. and limitations of existing face databases (Empirical)	28 community individuals – Age: 27.0 years – Sex: M=14; F=14 – Race: White and African	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise	73 images	– Colorful – Static
Vaiman et al. (2017)⁵⁴54 Vaiman M, Wagner MA, Caicedo E, Pereno GL. Development and validation of an Argentine set of facial expressions of emotion. Cogn Emot. 2017;31(2):249-60. https://doi.org/10.1080/02699931.2015.1098590 https://doi.org/10.1080/02699931.2015.10...	Argentina	Expresiones de Emociones Faciales (FACS)	The role of culture in recognizing emotions (Empirical)	14 Argentines from the community – Age: 25.53 years; SD=8.72 – Sex: M=8; F=6 – Race: ND	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	60 images	– Colorful – Static
Yang et al. (2020)⁵⁵55 Yang T, Yang Z, Xu G, Gao D, Zhang Z, Wang H, et al. Tsinghua facial expression database – a database of facial expressions in Chinese young and older women and men: development and validation. PLoS One. 2020;15(4):e0231304. https://doi.org/10.1371/journal.pone.0231304 https://doi.org/10.1371/journal.pone.023...	China	Tsinghua facial expression database	The role of culture in recognizing emotions (Empirical)	63 young and 47 elderly Chinese natives with an interest in acting Young – Age: 23.82 years; SD=4.18 – Sex: M=32; F=31 – Race: Chinese Elderly – Age: 64.40 years; SD=3.51 – Sex: M=21; F=26 – Race: Chinese	1) Happiness 2) Sadness 3) Fear 4) Disgust 5) Anger 6) Surprise 7) Neutral	880 images	– Colorful – Static

Authors and year of publication	Name of the database elaborated	Method used to elicit the emotions	Patterns in stimulus capture	Criteria used in the validation stage for inclusion of stimuli in the final database	Sample characteristics in the stage for the validation of the stimuli	Psychometric properties assessed
Benda and Scherf. (2020)²⁵25 Benda MS, Scherf KS. The complex emotion expression database: a validated stimulus set of trained actors. PLoS One. 2020;15(2):e0228248. https://doi.org/10.1371/journal.pone.0228248 https://doi.org/10.1371/journal.pone.022...	Complex Emotion Expression Database (CEED)	1) Presentation of an equivalent photograph expressing the emotion 2) Emotions elicited from specific situations	– Background: White – Clothes: ND – Distractors removed: ND	Accuracy ≥50%	796 volunteers recruited through MTurk – Age: 34. years; SD=11.6 – Gender: M=403; F=388 – Race: ND	– Analysis of the items: Accuracy^* * Only images with ≥50% accuracy were included in the final database; – Validity evidence: Content-based: Accuracy and error in each item
Chung et al. (2019)²⁶26 Chung KM, Kim S, Jung WH, Kim Y. Development and validation of the Yonsei face database (YFace DB). Front Psychol. 2019;10:2626. https://doi.org/10.3389/fpsyg.2019.02626 https://doi.org/10.3389/fpsyg.2019.02626...	Yonsei Face Database (YFace DB)	1) Presentation of an equivalent photograph expressing the emotion 2) Instruction on muscle movement of the emotions based on the FACS 3) Emotions elicited from specific situations	– Background: White – Clothes: Black T-shirt – Distractors removed: Beards, glasses, makeup, and bangs	Accuracy, intensity, and naturalness	212 students from the Seoul University – Age: 18-28 years – Gender: M=97; F=115 – Race: ND	– Analysis of the items: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Precision: Accuracy – Validity evidence: Content-based: Accuracy Based on the relationship with other variables: ANOVA for difference in precision between genders of the stimuli and evaluators, t-test for difference in mean accuracy between genders and emotions, and post-hoc Bonferroni analysis for items with significant differences‡
Conley et al. (2018)¹⁶16 Conley MI, Dellarco DV, Rubien-Thomas E, Cohen AO, Cervera A, Tottenham N, et al. The racially diverse affective expression (RADIATE) face stimulus set. Psychiatry Res. 2018;270:1059-67. https://doi.org/10.1016/j.psychres.2018.04.066 https://doi.org/10.1016/j.psychres.2018....	The racially diverse affective expression (RADIATE)	Presentation of an equivalent photograph expressing the emotion	– Background: White – Clothes: White sheet – Distractors removed: Glasses, headband, hats	Accuracy and Cohen's kappa	662 participants recruited through MTurk – Age: 18-35 years (27.6 years; SD=3.8) – Gender: M=402; F=260 – Race: Asian (n=48), Black/African-American (n=70), Caucasian (n=470), Hispanic (n=63), and others (n=11)	– Precision: Reliability (test-retest)^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; ; Cohen's kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and variability in precision by race of the model
Dalrymple et al. (2013)²⁷27 Dalrymple KA, Gomez J, Duchaine B. The dartmouth database of children's faces: acquisition and validation of a new face stimulus set. PLoS One. 2013;8(11):e79131. https://doi.org/10.1371/journal.pone.0079131 https://doi.org/10.1371/journal.pone.007...	The Dartmouth Database of Children's Faces	Emotions elicited from specific situations	– Background: Black – Clothes: Black dresses and black hats – Distractors removed: Glasses and jewelry	Images recognized with ≥70% accuracy	163 students and members of the Dartmouth College academic community – Age: 19.6 years; SD=4.15 – Gender: M=67; F=96 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and Cohen's kappa among the evaluators^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and Cohen's kappa among the evaluators Based on the relationship with other variables: ANOVA for difference in precision between gender of the stimuli and evaluators‡
Donadon et al. (2019)²⁸28 Donadon MF, Martin-Santos R, Osório FL. Baby faces: development and psychometric study of a stimuli set based on babies’ emotions. J Neurosci Methods. 2019;311:178-85. https://doi.org/10.1016/j.jneumeth.2018.10.021 https://doi.org/10.1016/j.jneumeth.2018....	Baby Faces	The parents were instructed and trained to provoke the intended emotions	ND	Rasch model to minimize floor and ceiling effects with values from 0.50 to 1.50 Rate of correct answers according to Kringelbach et al. 2008⁶⁴64 Kringelbach ML, Lehtonen A, Squire S, Harvey AG, Craske MG, Holliday IE, et al. A specific and rapid neural signature for parental instinct. PLoS One. 2008;3(2):e1664. https://doi.org/10.1371/journal.pone.0001664 https://doi.org/10.1371/journal.pone.000...	Validation 119 volunteers from the community – Age: 36 years; SD=12.8 – Gender: M=36.1%; F=63.9% – Race: Caucasian (n=69.7%), Black (n=26.1%), and Japanese (n=4.2%) Retest 31 volunteers from the community – Age: 38.06 years; SD=11.57 – Gender: M=35.5%; F=64.5% – Race: Caucasian (n=74%), Black (n=19.5%), and Japanese (n=6.5%)	– Analysis of the items: Adjustment and difficulty of the items by the Rasch model – Precision: Reliability (test-retest)^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy^§ § The mean rate of correct identification of the emotions was 62.5%; //Only images recognized by ≥15 evaluators were included in the final database; Based on the relationship with other variables: ANCOVA to assess the differences between groups considering the sociodemographic variables (gender, race, schooling level of the adults, and gender and race of the faces in the stimulus)‡
Ebner et al. (2010)¹³13 Ebner NC, Riediger M, Lindenberger U. FACES--a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav Res Methods. 2010;42(1):351-62. https://doi.org/10.3758/BRM.42.1.351 https://doi.org/10.3758/BRM.42.1.351...	Faces--a life-span Database of Facial Expressions	1) Emotion induction through photographs and videos 2) Emotions elicited from specific situations	– Background: Gray – Clothes: Gray T-shirt – Distractors removed: Jewelry, glasses, and makeup	Agreement among evaluators for (1) purity of the facial expression and (2) high intensity facial expression	154 students – Age: 20-81 years – Gender: M=78; F=76 – Race: Caucasian	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and consensus among the evaluators^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and consensus among the evaluators Based on the relationship with other variables: ANOVA for face age × evaluator's age × emotion expressed‡
Egger et al. (2011)²⁹29 Egger HL, Pine DS, Nelson E, Leibenluft E, Ernst M, Towbin KE, et al. The NIMH Child Emotional Faces Picture Set (NIMH-ChEFS): a new set of children's facial emotion stimuli. Int J Methods Psychiatr Res. 2011;20(3):145-56. https://doi.org/10.1002/mpr.343 https://doi.org/10.1002/mpr.343...	NIMH Child Emotional Faces Picture Set (NIMH-ChEFS)		– Background: Gray – Clothes: ND – Distractors removed: ND	The cutoff point for the image to be included was that ≥15 evaluators identified the intended emotion	20 professors and employees of the Duke University Medical Center – Age: 38.3 years – Gender: M=7; F=13 – Race: ND	– Analysis of the items: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Difficulty of the items: Intensity and representativeness scores – Precision: Agreement among the evaluators^// – Validity evidence: Content-based: Accuracy and agreement among the evaluators
Ekman and Friesen. (1976)³⁰30 Ekman P, Friesen WV. Pictures of facial affect. Palo Alto: Consulting Psychologists Press; 1976.	Pictures of Facial Affect (POFA)	Instruction on muscle movement of the emotions based on FACS	ND	ND	ND	ND
Fujimura and Umemura (2018)³¹31 Fujimura T, Umemura H. Development and validation of a facial expression database based on the dimensional and categorical model of emotions. Cogn Emot. 2018;32(8):1663-70. https://doi.org/10.1080/02699931.2017.1419936 https://doi.org/10.1080/02699931.2017.14...	A facial expression database based on the dimensional and categorical model of emotions	1) Emotions elicited from specific situations 2) Instruction on muscle movement of the emotions based on FACS	– Background: White – Clothes: White T-shirt – Distractors removed: Glasses and strong makeup	Agreement among the evaluators Mean of 69% agreement among the evaluators (SD=21%)	39 university students – Age: 21.33 years; SD=2.39 – Gender: M=19; F=20 – Race: Japanese natives	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and confusion matrix of agreement rates for images of dynamic and static expressions of each model
Franz et al. (2021)³²32 Franz M, Müller T, Hahn S, Lundqvist D, Rampoldt D, Westermann JF, et al. Creation and validation of the Picture-Set of Young Children's Affective Facial Expressions (PSYCAFE). PLoS One. 2021;16(12):e0260871. https://doi.org/10.1371/journal.pone.0260871 https://doi.org/10.1371/journal.pone.026...	Picture-Set of Young Children's Affective Facial Expressions (PSYCAFE)	1) Guidance of emotions in theater workshops 2) Directed Facial Action Task used to guide the movement of anatomical landmarks	– Background: White – Clothes: ND (just face) – Distractors removed: ND (just face)	Step 1 Confirmatory hierarchical cluster analysis by Ward Step 2 Intensity, authenticity, and likeability. Accuracy (77-100%) and AFFDEX Software	Step 1 197 volunteers from the community – Age: 32.9 years; SD=16.1 – Gender: M=33%; F=67% – Race: ND Step 2 44 volunteers from the community – Age: 25.7 years; SD=5.9) – Gender: M=48%; F=52% – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Based on the relationship with other variables: Stimulus age × expressed emotion × accuracy
Garrido et al. (2017)³³33 Garrido MV, Lopes D, Prada M, Rodrigues D, Jerónimo R, Mourão RP. The many faces of a face: comparing stills and videos of facial expressions in eight dimensions (SAVE database). Behav Res Methods. 2017;49(4):1343-60. https://doi.org/10.3758/s13428-016-0790-5 https://doi.org/10.3758/s13428-016-0790-...	Stills and Videos of facial Expressions (SAVE database)	Emotions elicited from specific situations	– Background: Gray – Clothes: White T-shirt – Distractors removed: Jewelry, glasses, and makeup	Stimuli with an assessment of 2.5 SD above or below the mean	120 university students – Age: 20.62 years; SD=3.39 – Gender: M=22.5%; F=77.5% – Race: Caucasian	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and interest dimensions (valence, excitement, clarity, intensity, appeal, similarity, and familiarity) Based on the relationship with other variables: Accuracy × gender of the model and the participant^¶ ¶ There was no significant difference in precision between the analyzed variables;
Giuliani et al. (2017)¹⁵15 Giuliani NR, Flournoy JC, Ivie EJ, Von Hippel A, Pfeifer JH. Presentation and validation of the DuckEES child and adolescent dynamic facial expressions stimulus set. Int J Methods Psychiatr Res. 2017;26(1):e1553. https://doi.org/10.1002/mpr.1553 https://doi.org/10.1002/mpr.1553...	The DuckEES child and adolescent dynamic facial expressions stimulus set	Emotions elicited from specific situations	– Background: White – Clothes: ND – Distractors removed: ND	Images recognized with ≥70% accuracy	36 volunteers from the Oregon University – Age: 19.5 years; SD=1.95 – Gender: M=14; F=22 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and Fleiss’ kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables;
Happy et al. (2015)³⁴34 Happy SL, Patnaik P, Routray A, Guha R. The Indian spontaneous expression database for emotion recognition. IEEE Transactions on Affective Computing. 2015;8(1):131-42. https://doi.org/10.1109/TAFFC.2015.2498174 https://doi.org/10.1109/TAFFC.2015.24981...	The Indian Spontaneous Expression Database for Emotion Recognition (ISED)	Emotion induction through videos	– Background: ND – Clothes: ND – Distractors removed: ND	Agreement among the evaluators (Fleiss’ Kappa)	Four trained evaluators – Age: ND – Gender: M=2; F=2 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and Fleiss’ kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables;
Kaulard et al. (2012)³⁵35 Kaulard K, Cunningham DW, Bülthoff HH, Wallraven C. The MPI facial expression database--a validated database of emotional and conversational facial expressions. PLoS One. 2012;7(3):e32321. https://doi.org/10.1371/journal.pone.0032321 https://doi.org/10.1371/journal.pone.003...	The MPI Facial Expression Database	Emotions elicited from specific situations	– Background: Black – Clothes: Black cape and hats – Distractors removed: Makeup and beards	Consistency among the evaluators (Fleiss’ Kappa)	20 German natives – Age: 19-33 years – Gender: M=10; F=10 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and Fleiss’ kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables;
Keutmann et al. (2015)³⁶36 Keutmann MK, Moore SL, Savitt A, Gur RC. Generating an item pool for translational social cognition research: methodology and initial validation. Behav Res Methods. 2015;47(1):228-34. https://doi.org/10.3758/s13428-014-0464-0 https://doi.org/10.3758/s13428-014-0464-...	Visual and vocal emotional expressions of adult and child actors	Emotions elicited from specific situations	– Background: Green – Clothes: ND – Distractors removed: ND	Accuracy	510 students, 226 from Drexel University and 284 from the University of Central Florida – Age: ND – Gender: ND – Race: ND	– Analysis of the items: Difficulty analysis and item discrimination by means of the classical test theory – Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy
Kim et al. (2017)³⁷37 Kim SM, Kwon YJ, Jung SY, Kim MJ, Cho YS, Kim HT, et al. Development of the Korean facial emotion stimuli: Korea university facial expression collection 2nd edition. Front Psychol. 2017;8:769. https://doi.org/10.3389/fpsyg.2017.00769 https://doi.org/10.3389/fpsyg.2017.00769...	Korea University Facial Expression Collection – Second Edition (KUFEC-II)	Instruction on muscle movement of the emotions based on FACS	– Background: Gray – Clothes: Pattern – Distractors removed: Makeup, accessories, and dyed hair	Internal consistency Accuracy	75 evaluators – Age: 19-69 years (26.17 years, SD=5.69) – Gender: M=39; F=36 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy; agreement among the evaluators^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and scores for purity, valence, and intensity Based on the relationship with other variables: ANOVA to test the effects of gender on recognition‡ and correlations between the participant's emotional state and task performance^¶ ¶ There was no significant difference in precision between the analyzed variables;
Langner et al. (2010)³⁸38 Langner O, Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, van Knippenberg A. Presentation and validation of the Radboud Faces Database. Cognition and Emotion. 2010;24(8):1377-88. https://doi.org/10.1080/02699930903485076 https://doi.org/10.1080/0269993090348507...	Radboud Faces Database	Instruction on muscle movement of the emotions based on FACS	– Background: White – Clothes: Black T-shirt – Distractors removed: Glasses, earrings and makeup	Accuracy	276 students from Radboud University – Age: 21.2 years; SD=4.0 – Gender: M=38; F=238 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and dimensions of interest (type of expression, intensity, clarity, genuineness, and valence) Based on the relationship with other variables: ANOVA comparing each of the precision variables with age, gender, expression, and gaze direction‡
LoBue and Thrasher. (2015)¹⁴14 LoBue V, Thrasher C. The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults. Front Psychol. 2015;5:1532. https://doi.org/10.3389/fpsyg.2014.01532 https://doi.org/10.3389/fpsyg.2014.01532...	The Child Affective Facial Expression (CAFE)	Instruction on muscle movement of the emotions based on FACS was carried out during improvised games	– Background: White – Clothes: White sheet – Distractors removed: ND	Images recognized with ≥60% accuracy	– 100 undergraduate students from Rutgers University – Age: ND – Gender: M=50; F=50 – Race: African-American (n=17%), Asian (n=27%), White (n=30%), Latin (n=17%), and others (n=9%)	– Analysis of the items: Difficulty of the items: Rasch model – Precision: Test-retest reliability^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and accuracy^# # The mean rate of correct identification of the emotions was 66%; – Validity evidence: Content-based: Accuracy
Lundqvist et al. (1998)³⁹39 Lundqvist D, Flykt A, Öhman A. The Karolinska directed emotional faces--KDEF. (CD ROM). Stockholm: Karolinska Institute, Department of Clinical Neuroscience, Psychology Section; 1998.	Karolinska Directed Emotional Faces (KDEF) Database	The participants were free to express the emotion as they wished	Background: Neutral Clothes: Gray T-shirt Distractors removed: Beard, mustache, earrings, glasses, and makeup	ND	ND	ND
Ma et al. (2020)⁴⁰40 Ma J, Yang B, Luo R, Ding X. Development of a facial-expression database of Chinese Han, Hui and Tibetan people. Int J Psychol. 2020;55(3):456-64. https://doi.org/10.1002/ijop.12602 https://doi.org/10.1002/ijop.12602...	Han, Hui, and Tibetan Chinese facial expression database	1) Emotion induction through photographs and videos 2) Instruction on muscle movement of the emotions based on FACS	– Background: Black – Clothes: ND – Distractors removed: Jewelry	Images recognized with ≥60% accuracy	– 240 volunteers (80 from each study region) – Age: 23 years; SD=1.7 – Gender: M=120; F=120 – Race: Chinese	– Precision: Accuracy^ Only images with ≥60% accuracy were included in the final; and method of halves – Validity evidence: Content-based: Accuracy Based on internal consistency: Cronbach's alpha^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables;
Ma et al. (2015)⁴¹41 Ma DS, Correll J, Wittenbrink B. The Chicago face database: a free stimulus set of faces and norming data. Behav Res Methods. 2015;47(4):1122-35. https://doi.org/10.3758/s13428-014-0532-5 https://doi.org/10.3758/s13428-014-0532-...	Chicago Face Database (CFD)	1) Emotions expressed from verbal instructions 2) Presentation of an equivalent photograph expressing the emotion	– Background: White – Clothes: Gray T-shirt – Distractors removed: ND	Two independent judges assessed how believable the expression was on a Likert scale from 1 to 9 (1=not at all believable; 9=very believable)	1,087 evaluators (convenience sample) – Age: 26.7 years; SD=10.5 – Gender: M=308; F=552 – Race: White (n=516), Asian (n=117), Black (n=74), bi–or multi-race (n=72), Latin (n=57), others (n=18), and did not report (n=233)	– Precision: Accuracy – Validity evidence: Based on the internal structure: exploratory factor analysis (Varimax rotation) Content-based: Accuracy; agreement among the evaluators^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and effects of race and gender of the stimuli (criteria for item construction)
Maack et al. (2017)⁴²42 Maack JK, Bohne A, Nordahl D, Livsdatter L, Lindahl ÅAW, Øvervoll M, et al. The Tromso Infant Faces Database (TIF): development, validation and application to assess parenting experience on clarity and intensity ratings. Front Psychol. 2017;8:409. https://doi.org/10.3389/fpsyg.2017.00409 https://doi.org/10.3389/fpsyg.2017.00409...	The Tromso Infant Faces Database (TIF)	The parents were instructed to elicit the intended emotions with games and specific stimuli	– Background: White – Clothes: White overalls and hat – Distractors removed: ND	The photographs with best agreement among the evaluators were selected Mean classification of clarity and intensity below 2.5 Validation: (a) expression portrayed, (b) clarity of expression, (c) intensity of the expression, and (d) valence of the expression	720 participants – Age: 18-70 years (32.8 years; SD=10.4) – Gender: M=21%; F=79% – Race: ND	– Precision: Accuracy^†† †† Accuracy is presented for each emotion and varied from 44 to 100%; – Validity evidence: Content-based: dimensions of interest (type of expression, clarity, intensity, and valence) Based on the relationship with other variables: ANOVA to compare performance × child-rearing stage × gender × mood^¶ ¶ There was no significant difference in precision between the analyzed variables;
Meuwissen et al. (2017)⁴³43 Meuwissen AS, Anderson JE, Zelazo PD. The creation and validation of the developmental emotional faces stimulus set. Behav Res Methods. 2017;49(3):960-6. https://doi.org/10.3758/s13428-016-0756-7 https://doi.org/10.3758/s13428-016-0756-...	Developmental Emotional Faces Stimulus Set (DEFSS)	1) Emotions elicited from specific situations 2) Presentation of an equivalent photograph expressing the emotion	– Background: Gray – Clothes: ND – Distractors removed: Jewelry	The images recognized by less of 55% of the evaluators were excluded	228 university students between undergraduate and graduate levels and children preappointed by the family via the Internet – Age: 8-30 years – Gender: M=150; F=254 Race: White (n=81%), non-White (n=17%)	– Precision: Accuracy^‡‡ ‡‡ Only images recognized by at least 55% of the evaluators were included in the final database. The mean recognition of the final database was 63%; – Validity evidence: Content-based: correct answers by age group, intensity, and emotion
Minear and Park. (2004)⁴⁴44 Minear M, Park DC. A lifespan database of adult facial stimuli. Behav Res Methods Instrum Comput. 2004;36(4):630-3. https://doi.org/10.3758/bf03206543 https://doi.org/10.3758/bf03206543...	A life span database of adult facial stimuli	Emotions expressed from verbal instructions	– Background: Gray – Clothes: ND – Distractors removed: ND	ND	ND	ND
Negrão et al. (2021)⁴⁵45 Negrão JG, Osorio AAC, Siciliano RF, Lederman VRG, Kozasa EH, D'Antino MEF, et al. The child emotion facial expression set: a database for emotion recognition in children. Front Psychol. 2021;12:666245. https://doi.org/10.3389/fpsyg.2021.666245 https://doi.org/10.3389/fpsyg.2021.66624...	The Child Emotion Facial Expression Set	1) Presentation of an equivalent photograph expressing the emotion 2) Emotions elicited from specific situations	– Background: White – Clothes: White – Distractors removed: ND	Step 1: 100% agreement between two evaluators Step 2: 100% agreement between other two evaluators (two of each step)	Four judges – Age: ND – Gender: ND – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and Cohen's kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Based on the relationship with other variables: accuracy × gender × age^¶ ¶ There was no significant difference in precision between the analyzed variables; ; emotion × race‡
Novello et al. (2018)⁴⁶46 Novello B, Renner A, Maurer G, Musse S, Arteche A. Development of the youth emotion picture set. Perception. 2018;47(10-11):1029-42. https://doi.org/10.1177/0301006618797226 https://doi.org/10.1177/0301006618797226...	Youth Emotion Picture Set	1) Emotions elicited from specific situations 2) Presentation of an equivalent photograph expressing the emotion 3) Presentation of videos and a game to specifically elicit the emotion of anger	– Background: ND – Clothes: Black cape – Distractors removed: Jewelry	Images recognized with ≥75% accuracy	Adults: 101 volunteers recruited through the snowball method – Age: 18-77 years – Gender: M=31.7%; F=68.3% – Race: ND Adolescents: 54 volunteers from state schools – Age: 12-17 years – Gender: M=40.7%; F=59.3% – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and Cohen's kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Based on the relationship with other variables: comparison of performance by age^¶ ¶ There was no significant difference in precision between the analyzed variables;
O'Reilly et al. (2016)⁴⁷47 O'Reilly H, Pigat D, Fridenson S, Berggren S, Tal S, Golan O, et al. The EU-emotion stimulus set: a validation study. Behav Res Methods. 2016;48(2):567-76. https://doi.org/10.3758/s13428-015-0601-4 https://doi.org/10.3758/s13428-015-0601-...	The EU-Emotion Stimulus Set	Emotions elicited from specific situations	– Background: White – Clothes: ND – Distractors removed: ND	Accuracy	1,231 volunteers – Age: 44 years; SD=16.7 – Gender: M=428; F=803 – Race: ND	– Precision: Accuracy_ and Cohen's kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: performance comparison by expression type, valence, and excitation
Olszanowski et al. (2015)⁴⁸48 Olszanowski M, Pochwatko G, Kuklinski K, Scibor-Rylski M, Lewinski P, Ohme RK. Warsaw set of emotional facial expression pictures: a validation study of facial display photographs. Front Psychol. 2015;5:1516. https://doi.org/10.3389/fpsyg.2014.01516 https://doi.org/10.3389/fpsyg.2014.01516...	Warsaw Set of Emotional Facial Expression Pictures (WSEFEP)	Instruction on muscle movement of the emotions based on FACS	– Background: White – Clothes: Black T-shirt – Distractors removed: Beards, mustaches, earrings, and glasses	Agreement in recognition	1,362 participants – Age: 26.6 years; SD=11.6 – Gender: M=261; F=1,101 – Race: ND	– Precision: agreement among the evaluators – Validity evidence: Content-based: purity analysis and intensity coefficient
Passareli et al. (2018)⁴⁹49 Passarelli M, Masini M, Bracco F, Petrosino M, Chiorri C. Development and validation of the Facial Expression Recognition Test (FERT). Psychol Assess. 2018;30(11):1479-90. https://doi.org/10.1037/pas0000595 https://doi.org/10.1037/pas0000595...	Facial Expression Recognition Test (FERT)	Presentation of an equivalent photograph expressing the emotion	– Background: Black – Clothes: Black T-shirt – Distractors removed: ND	Unidimensional model	794 volunteers from the community – Age: 36.13 years; SD=13.79 – Gender: M=36.2%; F=63.8% – Race: ND	– Validity evidence: Based on the internal structure: factor analysis through the two-parameter Bayesian model – Based on the relationship with other variables; performance comparison between gender and age‡ – Analysis of the items: Discrimination and difficulty through the Item Response Theory (IRT)
Romani-Sponchiado et al. (2015)⁵⁰50 Romani-Sponchiado A, Sanvicente-Vieira B, Mottin C, Hertzog-Fonini D, Arteche A. Child Emotions Picture Set (CEPS): development of a database of children's emotional expressions. Psychology & Neuroscience. 2015;8(4):467-78. https://doi.org/10.1037/h0101430 https://doi.org/10.1037/h0101430...	Child Emotions Picture Set	Emotion induction through videos	– Background: ND – Clothes: ND – Distractors removed: ND	Images recognized with ≥60% accuracy	30 psychologists with experience in child development – Age: ND – Gender: ND – Race: ND	– Precision: Accuracy^ Only images with ≥60% accuracy were included in the final; and Fleiss’ Kappa^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Analysis of the items: Accuracy – Validity evidence: Content-based: Fleiss’ kappa; chi-square to compare the proportion of posed and spontaneous photographs
Samuelsson et al. (2012)⁵¹51 Samuelsson H, Jarnvik K, Henningsson H, Andersson J, Carlbring P. The Umeå university database of facial expressions: a validation study. J Med Internet Res. 2012;14(5):e136. https://doi.org/10.2196/jmir.2196 https://doi.org/10.2196/jmir.2196...	Umeå University Database of Facial Expressions	Instruction on muscle movement of the emotions based on FACS	– Background: ND – Clothes: ND – Distractors removed: Makeup	Accuracy	526 participants – Age: 18-73 years (37.7 years; SD=13.0) – Gender: M=157; F=369 – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: – Based on the relationship with other variables; performance comparison by gender and age^¶ ¶ There was no significant difference in precision between the analyzed variables;
Sharma and Bhushan. (2019)⁵²52 Sharma U, Bhushan B. Development and validation of Indian Affective Picture Database. Int J Psychol. 2019;54(4):462-7. https://doi.org/10.1002/ijop.12471 https://doi.org/10.1002/ijop.12471...	Indian Affective Picture	1) Presentation of an equivalent photograph expressing the emotion 2) Emotions elicited from specific situations	– Background: ND – Clothes: ND – Distractors removed: Beards, glasses, and makeup	Accuracy Intensity (9-point scale)	350 undergraduate students – Age: 20.58 years; SD=1.13 – Gender: M=320; F=30 – Race: ND	– Analysis of the items: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Based on the relationship with other variables: t-test to compare men's and women's performance^¶ ¶ There was no significant difference in precision between the analyzed variables;
Tottenham et al. (2009)¹²12 Tottenham N, Tanaka JW, Leon AC, McCarry T, Nurse M, Hare TA, et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 2009;168(3):242-9. https://doi.org/10.1016/j.psychres.2008.05.006 https://doi.org/10.1016/j.psychres.2008....	The NimStim set of facial expressions	Emotions expressed from verbal instructions	– Background: ND – Clothes: ND – Distractors removed: Makeup	Validity (accuracy and Cohen's kappa) and reliability	Group 1 47 university students – Age: 19.4 years (SD=1.2) – Gender: M=39; F=47 – Race: European-American (81%), African-American (6%), Asian-American (9%), and Hispanic-American (4%) Group 2 34 volunteers from the community – Age: 25.8 years (SD=4.1) – Gender: M=22; F=12 – Race: European-American (59%), African-American (18%), Asian-American (6%), Hispanic-American (6%), and other races (12%)	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and test-retest^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and test-retest
Tracy et al. (2009)⁵³53 Tracy JL, Robins RW, Schriber RA. Development of a FACS-verified set of basic and self-conscious emotion expressions. Emotion. 2009;9(4):554-9. https://doi.org/10.1037/a0015766 https://doi.org/10.1037/a0015766...	University of California, Davis, Set of Emotion Expressions (UCDS)	Instruction on muscle movement of the emotions based on FACS	– Background: Gray – Clothes: White T-shirt – Distractors removed: Jewelry	Accuracy (the most recognized emotion of each expression was included in the final database)	Study 1 175 undergraduate students – Age: ND – Gender: M=35%; F=65% – Race: ND Study 2 234 undergraduate students – Age: ND – Gender: M=21%; F=79% – Race: ND	– Analysis of the items: Accuracy^§§ §§ The mean recognition rate of the final database varied from 47 to 94%. – Validity evidence: Content-based: Accuracy and performance based on race and gender of stimulus
Vaiman et al. (2017)⁵⁴54 Vaiman M, Wagner MA, Caicedo E, Pereno GL. Development and validation of an Argentine set of facial expressions of emotion. Cogn Emot. 2017;31(2):249-60. https://doi.org/10.1080/02699931.2015.1098590 https://doi.org/10.1080/02699931.2015.10...	FACS	Emotions elicited from specific situations	– Background: Blue – Clothes: White T-shirt – Distractors removed: Hair back (hair up)	Images recognized with ≥70% accuracy	466 students from the Psychology School of the National University of Córdoba. – Age: 20.29 years; SD=4.33 – Gender: M=23%; F=79% – Race: ND	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Analysis of the items: Discrimination – Validity evidence: Based on the convergent relationship: Descriptive comparison of database performance vs. POFA database performance^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables;
Yang et al. (2020)⁵⁵55 Yang T, Yang Z, Xu G, Gao D, Zhang Z, Wang H, et al. Tsinghua facial expression database – a database of facial expressions in Chinese young and older women and men: development and validation. PLoS One. 2020;15(4):e0231304. https://doi.org/10.1371/journal.pone.0231304 https://doi.org/10.1371/journal.pone.023...	Tsinghua facial expression database	1) Emotions elicited from specific situations 2) Instruction on muscle movement of the emotions based on FACS	– Background: White – Clothes: ND – Distractors removed: Tattoos, piercings, jewelry, glasses, and makeup.	Images recognized with ≥70% accuracy	34 young individuals and 31 older adults, Chinese Young individuals – Age: 19-35 years (23.50 years; SD=4.41) – Gender: M=19; F=15 – Race: Chinese Older adults – Age: 58-72 years (65.06 years; SD=3.50) – Gender: M=13; F=18 – Race: Chinese	– Precision: Accuracy^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; and kappa agreement among the evaluators^† † Satisfactory indexes; ‡There was a significant difference in precision between the analyzed variables; – Validity evidence: Content-based: Accuracy and kappa agreement among the evaluators

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[1] Correspondence: Daiene de Morais; Email: daienemorais1@gmail.com.