Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition

• URL: http://arxiv.org/abs/2008.11401v2
• Date: Sat, 8 May 2021 10:26:16 GMT
• Title: Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
• Authors: Ping Liu, Yuewei Lin, Zibo Meng, Lu Lu, Weihong Deng, Joey Tianyi Zhou, and Yi Yang
• Abstract summary: We propose Point Adversarial Self Mining (PASM) to improve the recognition accuracy in facial expression recognition. PASM uses a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively.
• Score: 79.75964372862279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition. Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task, generating harder learning samples to refine the network. The searched position is highly adaptive since it considers both the statistical information of each sample and the teacher network capability. Other than being provided new learning materials, the student network also receives guidance from the teacher network. After the student network finishes training, the student network changes its role and acts as a teacher, generating new learning materials and providing stronger guidance to train a better student network. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively. Extensive experimental results validate the efficacy of our method over the existing state of the arts for facial expression recognition.

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