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Automatic affect recognition has applications in many areas such aseducation, gaming, software development, automotives, medical care, etc. but itis non trivial task to achieve appreciable performance on in-the-wild datasets. In-the-wild data sets though represent real-world scenarios better thansynthetic data sets, the former ones suffer from the problem of incompletelabels. Inspired by semi-supervised learning, in this paper, we introduce oursubmission to the Multi-Task-Learning Challenge at the 4th Affective BehaviorAnalysis in-the-wild (ABAW) 2022 Competition. The three tasks that areconsidered in this challenge are valence-arousal(VA) estimation, classificationof expressions into 6 basic (anger, disgust, fear, happiness, sadness,surprise), neutral, and the 'other' category and 12 action units(AU) numberedAU-{1,2,4,6,7,10,12,15,23,24,25,26}. Our method Semi-supervised Multi-taskFacial Affect Recognition titled SS-MFAR uses a deep residual network with taskspecific classifiers for each of the tasks along with adaptive thresholds foreach expression class and semi-supervised learning for the incomplete labels.Source code is available at https://github.com/1980x/ABAW2022DMACS.

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