Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition

• URL: http://arxiv.org/abs/2107.11061v1
• Date: Fri, 23 Jul 2021 07:46:14 GMT
• Title: Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition
• Authors: Shasha Mao, Guanghui Shi, Licheng Jiao, Shuiping Gou, Yangyang Li, Lin Xiong, Boxin Shi
• Abstract summary: We propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space. By comparing semantic and task class-relation graphs of each image, the confidence of its label distribution is evaluated. Experimental results demonstrate the proposed method is more effective than compared state-of-the-art methods.
• Score: 69.18918567657757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By utilizing label distribution learning, a probability distribution is assigned for a facial image to express a compound emotion, which effectively improves the problem of label uncertainties and noises occurred in one-hot labels. In practice, it is observed that correlations among emotions are inherently different, such as surprised and happy emotions are more possibly synchronized than surprised and neutral. It indicates the correlation may be crucial for obtaining a reliable label distribution. Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space. Inspired by inherently diverse correlations among word2vecs, the topological information among facial expressions is firstly explored in the semantic space, and each image is embedded into the semantic space. Specially, a class-relation graph is constructed to transfer the semantic correlation among expressions into the task space. By comparing semantic and task class-relation graphs of each image, the confidence of its label distribution is evaluated. Based on the confidence, the label distribution is amended by enhancing samples with higher confidence and weakening samples with lower confidence. Experimental results demonstrate the proposed method is more effective than compared state-of-the-art methods.

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