Improved Active Shape Model for Efficient Extraction of Facial Feature Points on Mobile Devices

Abstract

Detection of facial feature is fundamental for applications such as security, biometrics, 3D modeling, and facial expression recognition. Active Shape Model (ASM) is one of the most popular local texture models for face detection. This paper addresses issues related to face detection and implements an efficient extraction algorithm for facial landmarks suitable for use on mobile devices. The original ASM was modified to enhance its performance (1) improving the initialization model using the center of the eyes by utilizing a feature ma of RGB color information, (2) building a modified model definition and fitting more landmarks than the classical ASM, and (3) extending and building a 2-D profile model for detecting faces in input images. The new scheme was evaluated on experimental test set containing over 500 images of faces and found to successfully extract facial features, clearly outperforming the original ASM.