Head Pose Estimation via Multi-Task Cascade CNN

Chuan Tang, Xinrui Huang, Zhicheng Wang, Yu Zhao, Mingyu Xue, Xin Wang

Abstract: In our daily life, many face applications need to complete three tasks: face detection, facial landmark localization and head pose estimation. Currently, most methods accomplish these three tasks separately. Multi-task cascade convolution neural network(MTCNN) adpots the idea of casecading that combines face detection and face alignment. Inspired by MTCNN, we combine the three tasks of face detection, head pose estimation and key points detection under a cascade framework. Simultaneously, we increased the number of key points detected by MTCNN from 5 to 21. By training and testing our model on the WIDER and Umdfaces datasets, we explored the inherent correlation between these three facial tasks and demonstrated the excellent results of the model tested in an unconstrained environment.