Learning Facial Motion Representation with a Lightweight Encoder for Identity Verification
Zheng Sun, Andrew W. Sumsion, Shad A. Torrie, Dah-Jye Lee · Electronics · 2022
Deep learning became an important image classification and object detection technique more than a decade ago. It has since achieved human-like performance for many computer vision tasks. Some of them involve the analysis of human face for applications like facial recognition, expression recognition, and facial landmark detection. In recent years, researchers have generated and made publicly available many valuable datasets that allow for the development of more accurate and robust models for these important tasks. Exploiting the information contained inside these pretrained deep structures could open the door to many new applications and provide a quick path to their success. This research focuses on a unique application that analyzes short facial motion video for identity verification. Our proposed solution leverages the rich information in those deep structures to provide accurate face representation for facial motion analysis. We have developed two strategies to employ the information contained in the existing models for image-based face analysis to learn the facial motion representations for our application. Combining with those pretrained spatial feature extractors for face-related analyses, our customized sequence encoder is capable of generating accurate facial motion embedding for identity verification application. The experimental results show that the facial geometry information from those feature extractors is valuable and helps our model achieve an impressive average precision of 98.8% for identity verification using facial motion.