Face detection based on multi task learning and multi layer feature fusion
Yanan Zhang, Hongyu Wang, Fang Xu · 2017
Face detection and facial feature location are two key parts of face recognition system. Usually, these two links are treated as two separate tasks, ignoring the correlation between tasks. In addition, most of the face detection algorithms based on deep convolution neural networks focus only on high-level semantic information of the image, and do not take full advantage of the underlying details of the image. In order to further improve the performance of face detection, we propose a face detection algorithm based on multi task learning and multilayer feature fusion. The proposed method integrates three tasks, namely, face classification, facial feature location, and bounding box regression, into a framework that takes full advantage of the correlation between multiple tasks and performs simultaneous learning over multiple tasks. At the same time, in order to make full use of the low-level details and high-level semantic information of the image, multi layer feature fusion technology is adopted. Finally, we test it on the face detection evaluation database FDDB. Experimental results show that the proposed algorithm has good performance in face detection.