Face Recognition Using Self-Organizing Maps

As an active research area, face recognition has been studied for more than 20 years. Especially, after the September 11 terrorist attacks on the United States, security systems utilizing personal biometric features, such as, face, voice, fingerprint, iris pattern, etc. are attracting a lot of attention. Among them, face recognition systems have become the subject of increased interest (Bowyer, 2004). Face recognition seems to be the most natural and effective method to identify a person since it is the same as the way human does and there is no need to use special equipments. In face recognition, personal facial feature extraction is the key to creating more robust systems. A lot of algorithms have been proposed for solving face recognition problem. Based on the use of the Karhunen-Loeve transform, PCA (Turk & Pentland, 1991) is used to represent a face in terms of an optimal coordinate system which contains the most significant eigenfaces and the mean square error is minimal. However, it is highly complicated and computational-power hungry, making it difficult to implement them into real-time face recognition applications. Feature-based approach (Brunelli & Poggio, 1993; Wiskott et al., 1997) uses the relationship between facial features, such as the locations of eye, mouth and nose. It can implement very fast, but recognition rate usually depends on the location accuracy of facial features, so it can not give a satisfied recognition result. There are many other algorithms have been used for face recognition. Such as Local Feature Analysis (LFA) (Penev & Atick, 1996), neural network (Chellappa et al., 1995), local autocorrelations and multi-scale integration technique (Li & Jain, 2005), and other techniques (Goudail et al., 1996; Moghaddam & Pentland, 1997; Lam & Yan, 1998; Zhao, 2000; Bartlett et al., 2002 ; Kotani et al., 2002; Karungaru et al., 2005; Aly et al., 2008) have been proposed. As a neural unsupervised learning algorithm, Kohonen’s Self-Organizing Maps (SOM) has been widely utilized in pattern recognition area. In this chapter, we will give an overview in SOM-based face recognition applications. Using the SOM as a feature extraction method in face recognition applications is a promising approach, because the learning is unsupervised, no pre-classified image data are needed at all. When high compressed representations of face images or their parts are formed by the SOM, the final classification procedure can be fairly simple, needing only a moderate number of labeled training samples. In this chapter, we will introduce various face recognition algorithms based on this consideration. 17