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Article overview
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Analysis of Semi-Supervised Methods for Facial Expression Recognition | | Shuvendu Roy
; Ali Etemad
; | | Date: |
1 Aug 2022 | | 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: this https URL | | Source: | arXiv, 2208.00544 | | Services: | Forum | Review | PDF | Favorites | |
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