Automatic Facial Expression Exaggeration System with Parallelized Implementation on a Multi-Core Embedded Computing Platform

Te-Feng Su, Chih-Hsueh Duan, Shu-Fan Wang, Yutzu Lee, Shang-Hong Lai

Abstract: In this paper, we propose an automatic facial expression exaggeration system, which consists of face detection, facial expression recognition, and facial expression exaggeration components, for generating exaggerated views of different expressions for an input face video. In addition, the parallelized algorithms for the automatic facial expression exaggeration system are developed to reduce the execution time on a multi-core embedded system. The experimental results show satisfactory expression exaggeration results and computational efficiency of the automatic facial expression exaggeration system under cluttered environments. The quantitative experimental comparisons show that the proposed parallelization strategies provide significant computational speedup compared to the single-processor implementation on a multi-core embedded platform.