Edge-Coupled and Multi-Dropout Face Alignment

Abstract

We propose the Edge-Coupled Multi-Dropout (ERN) framework for face alignment. Two features make the ERN framework an effective solution, one is the coupling of edges in the input and the other is the multiple dropout implemented at the convolution layers of the network. The core part of ERN consists of two component networks, the Edge Detection Network (EDN) and Mutiple Dropout Network (CD-VGG). Given a face, the EDN Detects the edges around the face and facial components where the facial landmarks are most likely located. The EDN output the detected facial edge and the face are then entered as input to the CD-VGG for locating the landmarks. The ERN framework also embeds a pose regressor following a face detector, making the collaboration of the EDN and CD-VGG a pose-oriented task. The ERN is tested on several benchmark databases, particularly on those with large poses, to emphasize the effectiveness for handling difficult cases.