Learning facial point response for alignment by purely convolutional network

Abstract

Face alignment is important for most facial analysis system. Regression based methods directly map the input face to shape space, make them sensitive to the face bounding boxes. In this work, we aim at developing a model that can deal with complex non-linear variations and be invariant to face bounding box distributions, while preserving high alignment accuracy. We define response map for each facial point, which is a 2D probability map indicating the presence likelihood of facial point at the corresponding locations. We solve the face alignment problem by two-stage processes. The first stage is response mapping stage, we use deep Purely Convolutional Network (a specialised Convolutional Neural Network designed for face alignment problem) to reconstruct the response maps. The second stage is shape mapping stage, which processes the response maps to get locations of facial key points. We explored four functions for this stage: max function, max + PCA, mean function and mean + PCA function. Experiments done on 300 W dataset show that our algorithm outperforms state-of-the-art methods.