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

doi:10.1590/1980-5764-dn-2022-0039

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Table of contents

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

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

Autoria SCIMAGO INSTITUTIONS RANKINGS

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)²⁵	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)²⁶	South Korea	Yonsei Face Database (YFace DB)	Basic emotions (Ekman and Friesen, 1975)⁵⁶	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 Open and closed mouth Varied intensities
Conley et al. (2018)¹⁶	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 *Open and closed mouth
Dalrymple et al. (2013)²⁷	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 *Happiness with closed mouth and happiness showing teeth
Donadon et al. (2019)²⁸	Brazil	Baby Faces	Ekman's Neurocultural Theory (1972)⁵⁷	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)¹³	Germany	Faces--a life-span Database of Facial Expressions	Age differences in emotion recognition (Ruffman et al., 2008)⁵⁸	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)²⁹	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 *Two directions of gazing: direct and avoided
Ekman and Friesen. (1976)³⁰	The United States	Pictures of Facial Affect (POFA)	Pan-cultural elements in facial expressions of emotions (Ekman et al., 1969)⁵	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)³¹	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)⁵⁹	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 Open and closed mouth Varied angles
Franz et al. (2021)³²	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 *Varied intensities
Garrido et al. (2017)³³	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)¹⁵	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)³⁴	India	The Indian Spontaneous Expression Database for Emotion Recognition (ISED)	Basic emotions (Ekman and Friesen, 1975)⁵⁶	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 *Varied intensities
Kaulard et al. (2012)³⁵	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 *Varied angles
Keutmann et al. (2015)³⁶	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 *Varied intensities
Kim et al. (2017)³⁷	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)³⁸	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 Three directions of gaze: front, right, and left Varied face angles
LoBue and Thrasher. (2015)¹⁴	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 *Open and closed mouth
Lundqvist et al. (1998)³⁹	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 *Varied face angles
Ma et al. (2020)⁴⁰	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)⁴¹	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 *Two directions of gaze: direct and averted
Maack et al. (2017)⁴²	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)⁴³	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)⁴⁴	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)⁴⁵	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)⁴⁶	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)⁴⁷	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)⁴⁸	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)⁴⁹	Italy	Facial Expression Recognition Test (FERT)	Basic emotions (Ekman e Friesen, 1975)⁵⁶ and Item Response Theory (Reise and Revicki, 2014)⁶³	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)⁵⁰	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 *Varied intensities
Samuelsson et al. (2012)⁵¹	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)⁵²	India	Indian Affective Picture	Basic emotions (Ekman and Friesen, 1975)⁵⁶ 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 *Varied face angles
Tottenham et al. (2009)¹²	The United States	The NimStim set of facial expressions	Basic emotions (Ekman and Friesen, 1975)⁵⁶ 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 *Open and closed mouth
Tracy et al. (2009)⁵³	Canada	Universidade da Califórnia, Davis, Set of Emotion Expressions (UCDS)	Basic emotions (Ekman and Friesen, 1975)⁵⁶ 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)⁵⁴	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)⁵⁵	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)²⁵	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* – Validity evidence: Content-based: Accuracy and error in each item
Chung et al. (2019)²⁶	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† – 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)¹⁶	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)† – Validity evidence: Content-based: Accuracy†; Cohen's kappa† and variability in precision by race of the model
Dalrymple et al. (2013)²⁷	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† and Cohen's kappa among the evaluators† – 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)²⁸	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. 2008⁶⁴	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)† – Validity evidence: Content-based: Accuracy§ 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)¹³	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† and consensus among the evaluators† – 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)²⁹	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† – 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)³⁰	Pictures of Facial Affect (POFA)	Instruction on muscle movement of the emotions based on FACS	ND	ND	ND	ND
Fujimura and Umemura (2018)³¹	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† – 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)³²	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† – Validity evidence: Based on the relationship with other variables: Stimulus age × expressed emotion × accuracy
Garrido et al. (2017)³³	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† – 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¶
Giuliani et al. (2017)¹⁵	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† – Validity evidence: Content-based: Accuracy and Fleiss’ kappa†
Happy et al. (2015)³⁴	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† – Validity evidence: Content-based: Accuracy and Fleiss’ kappa†
Kaulard et al. (2012)³⁵	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† – Validity evidence: Content-based: Accuracy and Fleiss’ kappa†
Keutmann et al. (2015)³⁶	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† – Validity evidence: Content-based: Accuracy
Kim et al. (2017)³⁷	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† – Validity evidence: Content-based: Accuracy; agreement among the evaluators† 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¶
Langner et al. (2010)³⁸	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† – 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)¹⁴	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† and accuracy# – Validity evidence: Content-based: Accuracy
Lundqvist et al. (1998)³⁹	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)⁴⁰	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** and method of halves – Validity evidence: Content-based: Accuracy Based on internal consistency: Cronbach's alpha†
Ma et al. (2015)⁴¹	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† and effects of race and gender of the stimuli (criteria for item construction)
Maack et al. (2017)⁴²	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†† – 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¶
Meuwissen et al. (2017)⁴³	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‡‡ – Validity evidence: Content-based: correct answers by age group, intensity, and emotion
Minear and Park. (2004)⁴⁴	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)⁴⁵	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† and Cohen's kappa† – Validity evidence: Based on the relationship with other variables: accuracy × gender × age¶; emotion × race‡
Novello et al. (2018)⁴⁶	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† and Cohen's kappa† – Validity evidence: Based on the relationship with other variables: comparison of performance by age¶
O'Reilly et al. (2016)⁴⁷	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† – Validity evidence: Content-based: performance comparison by expression type, valence, and excitation
Olszanowski et al. (2015)⁴⁸	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)⁴⁹	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)⁵⁰	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** and Fleiss’ Kappa† – 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)⁵¹	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† – Validity evidence: – Based on the relationship with other variables; performance comparison by gender and age¶
Sharma and Bhushan. (2019)⁵²	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† – Validity evidence: Based on the relationship with other variables: t-test to compare men's and women's performance¶
Tottenham et al. (2009)¹²	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† and test-retest† – Validity evidence: Content-based: Accuracy and test-retest
Tracy et al. (2009)⁵³	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§§ – Validity evidence: Content-based: Accuracy and performance based on race and gender of stimulus
Vaiman et al. (2017)⁵⁴	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† – Analysis of the items: Discrimination – Validity evidence: Based on the convergent relationship: Descriptive comparison of database performance vs. POFA database performance†
Yang et al. (2020)⁵⁵	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† and kappa agreement among the evaluators† – Validity evidence: Content-based: Accuracy and kappa agreement among the evaluators

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[TFN1] ND: not declared; M: male; F: female; SD: standard deviation.

[TFN2] ^*
Additional features of the face database.