Heterogeneous face detection based on multi‐task cascaded convolutional neural network

Abstract

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.