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Feature Elimination through Data Complexity for Error-Correcting Output Codes based micro-expression recognition
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AI
DOI：10.1016/j.image.2023.116962.png)
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Abstract
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Micro-Expression (ME) is a kind of short-lived and uncontrollable facial expressions. The MEs recognition task poses a great challenge to both the psychological and computer vision research communities. In this study, a Feature Elimination through Data Complexity-based Error-Correcting Output Codes (FEDC-ECOC) algorithm is proposed. In the generation of the coding matrix, a set of data complexity measures are utilized as the division criteria to form a coding matrix. Meanwhile, the sliding window and the greedy search algorithm are applied to improve the discriminative ability of the coding matrix for various emotion types. On the other hand, this study proposes a feature selection algorithm to identify essential features to enhance the performance of classifiers. Comprehensive experiments are conducted, and the results confirm the robustness and effectiveness of our FEDC-ECOC. Detailed analysis is given to further provide insights of the proposed method.
Keywords:
Error-Correcting Output Codes (ECOC)
Micro-Expression recognition
Data complexity
Feature elimination
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