A New Framework Combining Local-Region Division and Feature Selection for Micro-Expressions Recognition

• Published in: IEEE access Vol. 8; pp. 94499 - 94509
• Main Authors: Zhang, Yanliang, Jiang, Hanxiao, Li, Xingwang, Lu, Bing, Rabie, Khaled M., Rehman, Ateeq Ur
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computing time; Diversity reception; Face; Face recognition; Feature extraction; Feature recognition; Feature selection; Histograms; local region division; Micro-expression recognition; Psychology; ReliefF algorithm
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2995629

Micro-expressions are deliberate or unconscious movements of people's psychological activities, reflecting the transient facial true expressions. Previous works focus on the whole face for micro-expressions recognition. These methods can extract a number of feature vectors which are relevant or irrelevant to the micro-expressions recognition. Besides, the high-dimension feature vectors can result in longer computational time and increased computational complexity. In order to address these problems, we propose a new framework which combines the local-region division and the feature selection. Based on the proposed framework, the original images can retain more efficient regions and filter out the invalid components of feature vectors. Specifically, with the joint efforts of the facial deformation identification model and facial action coding system, the global region is divided into seven local regions with their corresponding actions units. The ReliefF algorithm is used to select effective components of feature vectors and reduce the dimension. To evaluate the proposed framework, we conduct experiments on both the Chinese Academy of Sciences Micro-expression II Database and Spontaneous Micro-expression Database with Leave-One-Subject-Out Cross Validation method. The results show that the performance in local combined regions outperforms its counterpart in the global region, and the recognition accuracy is further improved with the combination of feature selection.