Efficient Neural Architecture Search for Emotion Recognition

• URL: http://arxiv.org/abs/2303.13653v1
• Date: Thu, 23 Mar 2023 20:21:26 GMT
• Title: Efficient Neural Architecture Search for Emotion Recognition
• Authors: Monu Verma, Murari Mandal, Satish Kumar Reddy, Yashwanth Reddy Meedimale, Santosh Kumar Vipparthi
• Abstract summary: We propose to search for a highly efficient and robust neural architecture for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity.
• Score: 10.944807967751277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature variations in facial regions for both macro and micro-expressions requires expertise in network design. In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition. To the best of our knowledge, this is the first attempt to design a NAS-based solution for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. To maintain consistency between macro and micro-expressions, we utilize dynamic imaging and convert microexpression sequences into a single frame, preserving the spatiotemporal features in the facial regions. The EmoNAS has evaluated over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity.

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