2020/09/19 by Min Peng, Peng, Min, Chongyang Wang +12 · 1 citation
Computer Science · #Advanced Steganography and Watermarking Techniques #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer security #Computer vision #Expression (computer science) #FOS: Computer and information sciences #Frame (networking) #Key (lock) #Key frame #Music and Audio Processing #Telecommunications #Video Analysis and Summarization #cs.CV
paper · pdf · doi:10.48550/arxiv.2009.09179
Submitted for Review in IEEE Transactions on Multimedia
openalex publication_date 2020/09/19 · arxiv created 2021/03/15 · arxiv updated 2021/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human. In micro-expression, facial movement is transient and sparsely localized through time. However, the existing representation based on various deep learning techniques learned from a full video clip is usually redundant. In addition, methods utilizing the single apex frame of each video clip require expert annotations and sacrifice the temporal dynamics. To simultaneously localize and recognize such fleeting facial movements, we propose a novel end-to-end deep learning architecture, referred to as adaptive key-frame mining network (AKMNet). Operating on the video clip of micro-expression, AKMNet is able to learn discriminative spatio-temporal representation by combining spatial features of self-learned local key frames and their global-temporal dynamics. Theoretical analysis and empirical evaluation show that the proposed approach improved recognition accuracy in comparison with state-of-the-art methods on multiple benchmark datasets.