Development and validation of Indian Affective Picture Database

Sharma, Ujjwal; Bhushan, Braj

doi:10.1002/ijop.12471

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…This is supported by the N400 effect observed in the ERF analysis and the absence of significant decoding latency in the MVPA analysis. Both the studies (Sharma and Bhushan, 2019; Bhushan and Munshi, 2021) have reported higher accuracy rate for recognition of happiness, but this study confirms difference in terms of processing time as well.…”

Section: Discussionsupporting

confidence: 79%

“…The findings clearly show neurophysiological evidence for hemispheric and hemifacial asymmetry in the processing of facial expressions of emotions. The other two studies using the same database presented only the findings of behavioral data (Sharma and Bhushan, 2019) or mix of the behavioral data and geometric analysis of these expressions (Bhushan and Munshi, 2021). Both of them could not examine hemispheric or hemifacial asymmetry, although they had a unique database with facial expressions captured from multiple angles.…”

Section: Discussionmentioning

confidence: 99%

“…Another important finding of this study is the faster processing of happy emotion (facilitating the earlier decoding of visual angles, i.e., positive vs negative visual angle) compared to sad emotion. An earlier study using the IAPD (Sharma and Bhushan, 2019), found happy emotion to be the “easily identifiable emotion”. However, this was a behavioral study which reported a hit rate of 99.91 (SD 0.277) for happy and 94.18 (SD 2.99) for sad expressions.…”

Section: Discussionmentioning

confidence: 99%

“…The participants were presented with images eliciting happy and sad emotions at four different visual angles (i.e., - 45 0 , -90 0 , +45 0 and +90 0 ) selected from the Indian Affective Picture Database (IAPD) (Sharma and Bhushan, 2019). IAPD comprises 140 colored pictures modelling facial expressions of emotions at five different visual angles (−90, -45, +45, and +90).…”

Section: Methodsmentioning

confidence: 99%

“…The Indian Affective Picture Database (IAPD) (Sharma and Bhushan, 2019) and the Karolinska Directed Emotional Faces (KDEF) (Lundqvist et al, 1998) are the only databases that contain pictures of seven emotional expressions taken from four different angles, i.e., −90° (full left profile), −45° (half left profile), +45° (half right profile) and +90° (full right profile). However, the left (−90° and −45°) and right (+45° and +90°) profiles of these databases have yet to be examined for the left/right facial asymmetry that has been so well documented in behavioral studies and neurosciences.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Nara

Rathee

Molinaro

et al. 2021

Preprint

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Emotion processing has been a focus of research in psychology and neuroscience for some decades. While the evoked neural markers in human brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing has received less attention. Differences in processing speeds, that depend on emotion type, have not been determined. Furthermore, behavioral studies have found that the right side of the human face expresses emotions more accurately than the left side. Therefore, accounting for both the content of the emotion and the visual angle of presentation from the perspective of the viewer, here we have investigated variability in the discrimination of happy and sad faces when the visual angle of presentation was Positive (right side of the face) compared to Negative (left side of the face). Furthermore, the temporal dynamics involved in processing happy and sad emotions have been compared. Regardless of visual angle, happy emotions were processed faster than sad emotions. However, the evoked response to sad emotions significantly increased in amplitude compared to that for happy emotions, when faces were presented at Positive visual angles only. Source reconstruction from sensor-level ERFs show localized activities in ventral and dorsal stream, including fusiform gyrus, lingual gyrus, putamen and Pre and Post central gyrus. Multivariate pattern analysis (MVPA) confirmed these findings – demonstrating successful decoding of happy and sad emotions only occurred when the facial expression was viewed from a positive visual angle, and that happy emotions were processed faster than sad emotions.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Nara

Rathee

Molinaro

et al. 2021

Preprint

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Development and Validation of the AIIMS Facial Toolbox for Emotion Recognition

Verma

Kalsi

Shrivastava

et al. 2022

Indian Journal of Psychological Medicine

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Background: Emotional facial expression database, used in emotion regulation studies, is a special set of pictures with high social and biological relevance. We present the AIIMS Facial Toolbox for Emotion Recognition (AFTER) database. It consists of pictures of 15 adult professional artists displaying seven facial expressions—neutral, happiness, anger, sadness, disgust, fear, and surprise. Methods: This cross-sectional study enrolled 15 volunteer students from a professional drama college in India (six males and nine females; mean age = 26.2 ± 1.93 years). They were instructed to pose with different emotional expressions in high and low intensity. A total of 240 pictures were captured in a brightly lit room against a common, light background. Each picture was validated independently by 19 mental health professionals and two professional teachers of dramatic art. Apart from recognition of emotional quality, ratings were done for each emotion on a 5-point Likert scale with respect to three dimensions—intensity, clarity, and genuineness. Results are discussed in terms of mean scores on all four parameters. Results: The percentage hit rate for all the emotions, after exclusion of contempt, was 84.3%, with the mean kappa for emotional expression being 0.68. Mean scores on intensity, clarity, and genuineness of the emotions depicted in the pictures were high. Conclusions: The database would be useful in the Indian context for researching facial emotion recognition. It has been validated among a group of experts and was found to have high inter-rater reliability.

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Construction of face databases for tasks to recognize facial expressions of basic emotions: a systematic review

Fabrício

Ferreira

Maximiano-Barreto

et al. 2022

Dement. neuropsychol.

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ABSTRACT. Recognizing the other's emotions is an important skill for the social context that can be modulated by variables such as gender, age, and race. A number of studies seek to elaborate specific face databases to assess the recognition of basic emotions in different contexts. Objectives: This systematic review sought to gather these studies, describing and comparing the methodologies used in their elaboration. Methods: The databases used to select the articles were the following: PubMed, Web of Science, PsycInfo, and Scopus. The following word crossing was used: “Facial expression database OR Stimulus set AND development OR Validation.” Results: A total of 36 articles showed that most of the studies used actors to express the emotions that were elicited from specific situations to generate the most spontaneous emotion possible. The databases were mainly composed of colorful and static stimuli. In addition, most of the studies sought to establish and describe patterns to record the stimuli, such as color of the garments used and background. The psychometric properties of the databases are also described. Conclusions: The data presented in this review point to the methodological heterogeneity among the studies. Nevertheless, we describe their patterns, contributing to the planning of new research studies that seek to create databases for new contexts.

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Development and validation of Indian Affective Picture Database

Cited by 3 publications

References 13 publications

Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Development and Validation of the AIIMS Facial Toolbox for Emotion Recognition

Construction of face databases for tasks to recognize facial expressions of basic emotions: a systematic review

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