Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition

• URL: http://arxiv.org/abs/2410.17622v1
• Date: Wed, 23 Oct 2024 07:26:19 GMT
• Title: Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition
• Authors: Jie Song, Mengqiao He, Jinhua Feng, Bairong Shen,
• Abstract summary: We focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations. To further alleviate the scarcity of labeled and diverse images, we propose a Mixup-based data augmentation strategy.
• Score: 5.750927184237346
• License:
• Abstract: In recent years, Facial Expression Recognition (FER) has gained increasing attention. Most current work focuses on supervised learning, which requires a large amount of labeled and diverse images, while FER suffers from the scarcity of large, diverse datasets and annotation difficulty. To address these problems, we focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations to learn features of facial geometry and expression regions, followed by two-stage fine-tuning on FER datasets with limited labels. In addition, to further alleviate the scarcity of labeled and diverse images, we propose a Mixup-based data augmentation strategy tailored for facial images, and the loss weights of real and virtual images are determined according to the intersection-over-union (IoU) of the faces in the two images. Experiments on RAF-DB, AffectNet, and FERPlus show that our method outperforms existing semi-supervised FER methods and achieves new state-of-the-art performance. Remarkably, with only 5%, 25% training sets,our method achieves 64.02% on AffectNet,and 88.23% on RAF-DB, which is comparable to fully supervised state-of-the-art methods. Codes will be made publicly available at https://github.com/zhelishisongjie/SSFER.

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