automatic recognition of facial displays of unfelt emotions

• DOI: 10.48550/arxiv.1707.04061
• Publication Date: 2021-04-01
• Authors: Kaustubh Kulkarni; Ciprian Adrian Co...; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarj...
• Keywords: FOS: Computer and information sciences; Emotion recognition , Face recognition , Feature extraction , Face , Psychology , Observers , Trajectory; expresión facial sin emoción; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; expressió facial sense emoció; computació afectiva; 02 engineering and technology; análisis del comportamiento humano; Human face recognition (Computer science); 616; 0202 electrical engineering, electronic engineering, information engineering; facial expression recognition; Face recognition; Observers; affective computing; TrAffective computing; anàlisi del comportament humà; Reconeixement facial (Informàtica); reconeixement d'expressió facial; computación afectiva; Feature extraction; Reconocimiento facial (Informática); Emotion recognition; human behaviour analysis; unfelt facial expression of emotion; reconocimiento de la expresión facial

Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.