Face Alignment Based on Two-Stage Localization

Abstract

The traditional cascade regression model could easily drop into local optimum when the initial localization is far from real shape.This paper proposesa face alignment scheme based ontwo-stage localization. In theface detection stage, the multi-task CNN is adopted to get the information oflandmarks and poseas the initial localization for second stage. In the second stage, the cascade regression based on random forest is implemented for face alignment, and the localization from first stage is used as the initial coarse localization for regression model to solve the problem of initialization sensitivity. The scheme can achieve excellent performance even in the case of large pose variations and occlusion. Compared with the traditional method, experiments show that our algorithm can get state-of-art resultsin real-time on benchmark of 300W dataset.