Face Alignment Using Boosting and Evolutionary Search

Abstract

Abstract. In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.

Facial feature point detection: A comprehensive survey

“…The goal of the fitting procedure is to find the PDM parameter updates which maximize the score of the strong classifier. Zhang et al (2009) utilized granular features to replace the rectangular Haar-like feature to improve computational efficiency, discriminability and a larger search space. In addition, they explored the evolutionary search process to overcome the deficiency searching problem in the large feature space.…”