68 Face Feature Points Detection Based on Cascading Convolutional Neural Network with Small Filter

Abstract

Facial detection has received more and more attention in the past two decades. Due to the pose, occlusion, and illumination changes in the photo, the detection task is quite a challenge in an unconstrained environment. This paper proposed a cascaded convolutional neural network DCNNSF-CFC (Deep Convolution Neural Network with Small Filter-with Coarse-to-Fine Cascade) to localize large facial landmarks to improve the accuracy and robustness of network prediction which based on the original small filter deep convolutional neural network. Each network is trained separately to locally refine a subset of facial landmarks generated at the previous network level, and each geometric network is explicitly constrained to modify the input information of the current network. AFW, LFPW and Helen datasets were used in this study and it was proved to be more accurate and robust than DRMF, Opm, CNN and RCPR.