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Facial expression databases

Well-annotated (emotion-tagged) media content of facial behavior is essential for training, testing, and validation of algorithms for the development of expression recognition systems. The emotion annotation can be done in discrete emotion labels or on a continuous scale. Most of the databases are usually based on the basic emotions theory (by Paul Ekman), which assumes the existence of six discrete basic emotions (anger, fear, disgust, surprise, joy, sadness). However, some databases include the emotion tagging in continuous arousal-valence scale. And some databases include the AU activations based on FACS.

In posed expression databases, the participants are asked to display different basic emotional expressions, while in spontaneous expression database, the expressions are natural. Spontaneous expressions differ from posed ones remarkably in terms of intensity, configuration, and duration. Apart from this, synthesis of some AUs are barely achievable without undergoing the associated emotional state. Therefore, in most cases, the posed expressions are exaggerated, while the spontaneous ones are subtle and differ in appearance.

Many publicly available databases are categorized here.[1][2] Here are some details of the facial expression databases.

Database Facial expression Number of Subjects Number of images/videos Gray/Color Resolution, Frame rate Ground truth Type
Extended Cohn-Kanade Dataset (CK+)[3] download neutral, sadness, surprise, happiness, fear, anger, and disgust 123  593 image sequences (327 sequences having discrete emotion labels) Mostly gray 640* 490 Facial expression labels and FACS (AU label for final frame in each image sequence) Posed; spontaneous smiles
Japanese Female Facial Expressions (JAFFE)[4] download neutral, sadness, surprise, happiness, fear, anger, and disgust 10 213 static images Gray 256* 256 Facial expression label Posed
MMI Database[5] download 43 1280 videos and over 250 images Color 720* 576 AU label for the image frame with apex facial expression in each image sequence Posed and Spontaneous
Belfast Database[6] download Set 1 (disgust, fear, amusement, frustration, surprise) 114 570 video clips Color 720*576 Natural Emotion
Set 2 (disgust, fear, amusement, frustration, surprise, anger, sadness) 82 650 video clips Color
Set 3 (disgust, fear, amusement) 60 180 video clips Color 1920*1080
DISFA[7] download - 27 4,845 video frames Color 1024*768; 20 fps AU intensity for each video frame (12 AUs) Spontaneous
Multimedia Understanding Group (MUG)[8] download neutral, sadness, surprise, happiness, fear, anger, and disgust 86 1462 sequences Color 896*896, 19fps Emotion labels Posed
Indian Spontaneous Expression Database (ISED)[9] download sadness, surprise, happiness, and disgust 50 428 videos  Color 1920* 1080, 50 fps Emotion labels Spontaneous
Radboud Faces Database (RaFD)[10] download neutral, sadness, contempt, surprise, happiness, fear, anger, and disgust 67 Three different gaze directions and five camera angles (8*67*3*5=8040 images) Color 681*1024 Emotion labels Posed
FERG (Facial Expression Research Group Database)-DB[11] for stylized characters angry, disgust, fear, joy, neutral, sad, surprise 6 55767 Color 768x768 Emotion labels Frontal pose

ReferencesEdit

  1. ^ "collection of emotional databases". 
  2. ^ "facial expression databases". 
  3. ^ P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression," in 3rd IEEE Workshop on CVPR for Human Communicative Behavior Analysis, 2010
  4. ^ M. J. Lyons, M. Kamachi and J. Gyoba, "Japanese Female Facial Expressions (JAFFE)," Database of digital images, 1997
  5. ^ M. Valstar and M. Pantic, "Induced disgust, happiness and surprise: an addition to the MMI facial expression database," in Proc. Int. Conf. Language Resources and Evaluation, 2010
  6. ^ I. Sneddon, M. McRorie, G. McKeown and J. Hanratty, "The Belfast induced natural emotion database," IEEE Trans. Affective Computing, vol. 3, no. 1, pp. 32-41, 2012
  7. ^ S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh and J. Cohn., "DISFA: A Spontaneous Facial Action Intensity Database," IEEE Trans. Affective Computing, vol. 4, no. 2, pp. 151–160, 2013
  8. ^ N. Aifanti, C. Papachristou and A. Delopoulos, The MUG Facial Expression Database, in Proc. 11th Int. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Desenzano, Italy, April 12–14, 2010.
  9. ^ S L Happy, P. Patnaik, A. Routray, and R. Guha,  “The Indian Spontaneous Expression Database for  Emotion Recognition,” in IEEE Transactions on Affective Computing,  2016, doi:10.1109/TAFFC.2015.2498174.
  10. ^ Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. Cognition & Emotion, 24(8), 1377—1388. doi:10.1080/02699930903485076
  11. ^ "Facial Expression Research Group Database (FERG-DB)". grail.cs.washington.edu. Retrieved 2016-12-06.