Distributed Face Recognition Using Multiple Kernel Discriminant Analysis in Wireless Sensor Networks

This paper proposes a module based distributed wireless face recognition system by integrating multiple kernel discriminant analysis with face recognition in wireless sensor networks. By maximizing the margin maximization criterion (MMC), we separately perform an iterative scheme for kernel parameter optimization for each module. The simulation on the FERET and CMU PIE face databases shows that our multiple kernel framework and the optimization procedure achieve high recognition performance, compared with single-kernel-based KDDA. 1. Introduction Face recognition (FR) system is one of the most important biometric techniques and is used in a wide range of security applications such as access control, identification systems, and surveillance [1]. FR is a contactless biometric technique and has advantages of being natural and passive over other biometric techniques requiring cooperative subjects, such as fingerprint recognition and iris recognition [2]. A normal framework of FR system is shown in Figure 1, including procedures of enrollment and identification [3]. Figure 1: A normal framework of FR system. In recent years, FR systems combined with wireless sensor networks (WSNs) [4] have shown great interest, as WSNs are very helpful for contactless biometrics security applications. For example, Kim et al. implement a wireless face recognition system based on ZigBee protocol and principle components analysis (PCA) method with low energy consumption [5]. Muraleedharan et al. propose the use of a specific evolutionary algorithm to optimize routing in distributed time varying network for face recognition [6]. Chang and Aghajan focus on recovering face orientation for more robust face recognition in wireless image sensor networks [7]. Zaeri et al. propose application of face recognition for wireless surveillance systems [8]. As there exist many image variations such as pose, illumination, and facial expression, face recognition is a highly complex and nonlinear problem which could not be sufficiently handled by linear methods, such as principal components analysis (PCA) [9] and linear discriminant analysis (LDA) [10]. Therefore, it is reasonable to assume that a better solution to this inherent nonlinear problem could be achieved using nonlinear methods, such as the so-called kernel machine techniques [11]. Following the success of applying the kernel trick in support vector machines (SVMs) [12], many kernel-based PCA and LDA methods have been developed and applied in pattern recognition tasks, such as kernel PCA (KPCA) [13], kernel Fisher discriminant (KFD)