SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition

• URL: http://arxiv.org/abs/2207.09012v1
• Date: Tue, 19 Jul 2022 01:38:15 GMT
• Title: SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition
• Authors: Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar, S Balasubramanian
• Abstract summary: We introduce our submission to the Multi-Task-Learning Challenge at the 4th Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition. Our method Semi-supervised Multi-task Facial Affect Recognition titled bftextSS-MFAR uses a deep residual network with task specific classifiers for each of the tasks.
• Score: 3.823356975862006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve appreciable performance on in-the-wild data sets. In-the-wild data sets though represent real-world scenarios better than synthetic data sets, the former ones suffer from the problem of incomplete labels. Inspired by semi-supervised learning, in this paper, we introduce our submission to the Multi-Task-Learning Challenge at the 4th Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition. The three tasks that are considered in this challenge are valence-arousal(VA) estimation, classification of expressions into 6 basic (anger, disgust, fear, happiness, sadness, surprise), neutral, and the 'other' category and 12 action units(AU) numbered AU-\{1,2,4,6,7,10,12,15,23,24,25,26\}. Our method Semi-supervised Multi-task Facial Affect Recognition titled \textbf{SS-MFAR} uses a deep residual network with task specific classifiers for each of the tasks along with adaptive thresholds for each expression class and semi-supervised learning for the incomplete labels. Source code is available at https://github.com/1980x/ABAW2022DMACS.

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