Modelling and Analysis of Facial Expressions Using Optical Flow Derived Divergence and Curl Templates
AbstractFacial expressions are integral part of non-verbal paralinguistic communication as they provide cues significant in perceiving one’s emotional state. Assessment of emotions through expressions is an active research domain in computer vision due to its potential applications in multi-faceted domains. In this work, an approach is presented where facial expressions are modelled and analyzed with dense optical flow derived divergence and curl templates that embody the ideal motion pattern of facial features pertaining to unfolding of an expression on the face. Two types of classification schemes based on multi-class support vector machine and k-nearest neighbour are employed for evaluation. Promising results obtained from comparative analysis of the proposed approach with state-of-the-art techniques on the Extended Cohn Kanade database and with human cognition and pre-trained Microsoft face application programming interface on the Karolinska Directed Emotional Faces database validate the efficiency of the approach.
KeywordsFacial expression recognition, emotion analysis, optical flow, multi-class support vector classification, k-nearest neighbour classification, human cognition vs. machine analysis
Copyright (c) 2021 Shivangi Anthwal
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