A Novel ASM-Based Two-Stage Facial Landmark Detection Method

Abstract

The active shape model (ASM) has been successfully applied to locate facial landmarks. However, in some exaggerated facial expressions, such as surprise, laugh and provoked eyebrows, it is prone to make mistaken detection. To overcome this difficulty, we propose a two-stage facial landmark detection algorithm. In the first stage, we focus on detecting the individual salient corner-type facial landmarks by applying a commonly-used Adaboosting-based algorithm, and then further apply a global ASM to refine the positions of these landmarks iteratively. In the second stage, the individual detection results of the corner-type facial landmarks serve as the initial positions of active shape model which can be further iteratively refined by an ASM algorithm. Experimental results demonstrate that the proposed method can achieve very good performance in locating facial landmarks and it consistently and considerably outperforms the traditional ASM method.