Analysis of Semi-Supervised Methods for Facial Expression Recognition

• URL: http://arxiv.org/abs/2208.00544v1
• Date: Sun, 31 Jul 2022 23:58:35 GMT
• Title: Analysis of Semi-Supervised Methods for Facial Expression Recognition
• Authors: Shuvendu Roy, Ali Etemad
• Abstract summary: Training deep neural networks for image recognition often requires large-scale human annotated data. Semi-supervised methods have been proposed to reduce the reliance of deep neural solutions on labeled data. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets.
• Score: 19.442685015494316
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER

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