D. Richards and B.-H. Kang (Eds.): PKAW 2008, LNAI 5465, pp. 231–241, 2009. © Springer-Verlag Berlin Heidelberg 2009 Facial Feature Extraction Using Geometric Feature and Independent Component Analysis Toan Thanh Do 1 and Thai Hoang Le 2 1 Department of Computer Sciences, University of Natural Sciences, HCMC, Vietnam dttoan@fit.hcmuns.edu.vn 2 Department of Computer Sciences, University of Natural Sciences, HCMC, Vietnam lhthai@fit.hcmuns.edu.vn Abstract. Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recogni- tion, facial image compression. There are many methods have been proposed in the literature for the facial feature extraction task. However, all of them have still disadvantage such as not complete reflection about face structure, face tex- ture. Therefore, a combination of different feature extraction methods which can integrate the complementary information should lead to improve the effi- ciency of feature extraction stage. In this paper we describe a methodology for improving the efficiency of feature extraction stage based on the association of two methods: geometric feature based method and Independent Component Analysis (ICA) method. Comparison of two methods of facial feature extrac- tion: geometric feature based method combined with PCA method (called GPCA) versus geometric feature based method combined with ICA method (called GICA) on CalTech dataset has demonstrated the efficiency of GICA method. Our results show that GICA achieved good performance 96.57% com- pared to 94.70% of GPCA method. Furthermore, we compare two methods mentioned above on our dataset, with performance of GICA being 98.94% bet- ter 96.78% of GPCA method. The experiment results have confirmed the bene- fits of the association geometric feature based method and ICA method in facial feature extraction. Keywords: Face recognition; independent component analysis (ICA); principal component analysis (PCA); geometric features. 1 Introduction Face recognition has a variety of potential applications in public security, law en- forcement and commerce such as identity authentication for credit card or driver li- cense, access control, information security, and video surveillance, etc.. In addition, there are many emerging fields that can benefit from face recognition such as human- computer interfaces and e-services, including e-home, tele-shopping and tele-banking. However, one of the most difficult from face recognition problem is the facial feature extraction step. A good feature extraction will increase performance of face recognition system. Various techniques have been proposed in the literature for this purpose, and are mainly classified in four groups.