Sparse Spatiotemporal Descriptor for Micro-Expression Recognition Using Enhanced Local Cube Binary Pattern

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 16; p. 4437
• Main Authors: Cen, Shixin, Yu, Yang, Yan, Gang, Yu, Ming, Yang, Qing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.08.2020; MDPI
• Subjects: Algorithms; Automated Facial Recognition; Classification; Datasets; Deep learning; Emotions; enhanced local cube binary pattern; Face; Facial Expression; Human subjects; Humans; micro-expression recognition; multi-kernel support vector machine; multi-regional joint sparse learning; Regions; Support Vector Machine; Support vector machines; enhanced local cube binary pattern; micro-expression recognition; multi-kernel support vector machine; multi-regional joint sparse learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20164437

As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, and security. However, micro-expression recognition is a formidable challenge due to the short-lived time frame and low-intensity of the facial actions. In this paper, a sparse spatiotemporal descriptor for micro-expression recognition is developed by using the Enhanced Local Cube Binary Pattern (Enhanced LCBP). The proposed Enhanced LCBP is composed of three complementary binary features containing Spatial Difference Local Cube Binary Patterns (Spatial Difference LCBP), Temporal Direction Local Cube Binary Patterns (Temporal Direction LCBP), and Temporal Gradient Local Cube Binary Patterns (Temporal Gradient LCBP). With the application of Enhanced LCBP, it would no longer be a problem to provide binary features with spatiotemporal domain complementarity to capture subtle facial changes. In addition, due to the redundant information existing among the division grids, which affects the ability of descriptors to distinguish micro-expressions, the Multi-Regional Joint Sparse Learning is designed to perform feature selection for the division grids, thus paying more attention to the critical local regions. Finally, the Multi-kernel Support Vector Machine (SVM) is employed to fuse the selected features for the final classification. The proposed method exhibits great advantage and achieves promising results on four spontaneous micro-expression datasets. Through further observation of parameter evaluation and confusion matrix, the sufficiency and effectiveness of the proposed method are proved.