A Biologically Inspired Model in .NET

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68. I. T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 2002. 69. A. Hyv rinen, J. Karhunen, and E. Oja, Independent Component Analysis, Wiley-Interscience, a New York, 2001. 70. Y. Zhang and A. M. Martinez, A weighted probabilistic approach to face recognition from multiple images and video sequences, Image Vis. Comput. 24:626 638, 2006. 71. Y. Yacoob and L. Davis, Recognizing human facial expressions from long image sequences using optical ow, IEEE Trans. Pattern Anal. Mach. Intell. 18:636 642, 1996.
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Multimodal Biometrics Based on Near-Infrared Face Recognition
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Rui Wang, Shengcai Liao, Zhen Lei, and Stan Z. Li
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Biometric identi cation makes use of the physiological or behavioral characteristics of people, such as ngerprint, iris, face, palmprint, gait, and voice, for personal identi cation [1], which provides advantages over nonbiometric methods such as password, PIN, and ID cards. Its promising applications as well as the theoretical challenges have gotten its heated attraction from the last decade. Face recognition is a natural, nonintrusive and easy way for biometrics and has been one of the most popular techniques. However, most current face recognition systems are based on face images captured in visible light spectrum, which are compromised in accuracy by changes in environmental illumination. The near-infrared (NIR) face image-based recognition method [2 4] overcomes this problem. It is shown to be invariant to the changes of the visible lighting and hence is accurate and robust for face recognition. Recent research [5 9] has pointed out that multimodal biometric fusion can signi cantly improve the performance of the system due to the complementary information from different modalities helpful for classi cation. There exists various methods for multimodal fusion. Brunelli and Falavigna [10] proposed a person identi cation system based on voice and face, using a HyperBF network as the best performing
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Biometrics: Theory, Methods, and Applications. Edited by Boulgouris, Plataniotis, and Micheli-Tzanakou Copyright 2010 the Institute of Electrical and Electronics Engineers, Inc.
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Multimodal Biometrics Based on Near-Infrared Face Recognition
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fusion module. Kittler et al. [11] proposed a face and voice multimodal biometric system and developed a common theoretical framework for combing classi ers in reference 5 with several fusion techniques including sum, product, minimum, and maximum rules, where the best combination results are obtained for a simple sum rule. Hong and Jain [6] proposed an identi cation system based on face and ngerprint, where ngerprint matching is applied after pruning the database via face matching. Ross et al. [12] combined face, ngerprint, and hand geometry biometrics with sum, decision tree, and linear discriminant-based methods, where the sum rule achieves the best. Wang et al. [13], Son and Lee [14], and Chen and Chu [15] developed face and iris multimodal biometric systems, and different fusion methods were investigated. Kumar et al. [16] described a hand-based veri cation system that combined the geometric features of the hand with palmprints at the feature and matching score levels. Li et al. [9] proposed a systematic framework for fusing 2D and 3D face recognition at both feature and score levels, by exploring synergies of the two modalities at these levels and achieved good performance in large database. Chang et al. [7] combined ear and face biometrics with an appearancebased method. Ribaric and Fratric [8] described a biometric identi cation system based on eigenpalm and eigen nger features, with fusion applied at the matching score level. In this chapter we present a near-infrared (NIR) face-based approach for multimodal biometric fusion. The motivations for this approach are the following: (1) NIR face recognition overcomes problems arising from uncontrolled illumination in visible light (VL) image-based face biometric and achieves signi cantly better results than VL face; and (2) the fusion of NIR face with VL face or iris biometrics is a natural way for multibiometrics, because it is either face-based (NIR face + VL face) or NIR-based (NIR face+ irais). The NIR face is fused with VL face or iris modality at the matching score level. As for score level fusion, there are two common approaches. One is to treat it as a combination problem, in which the individual matching scores are combined according to some rule such as sum rule, max rule, or min rule to generate a single scalar score. The others is to formulate it as a classi cation problem, such as LDA [12] or a power series model (PSM)-based method [17]. The latter needs to be learned in a training set. We evaluate these fusion methods on real large multimodal databases we collected, in which NIR face and VL face or iris image for one subject are captured simultaneously by our own image capture device. The NIR database is publicly available on the web [18]. The experimental results show that the learning-based fusion methods such as LDA and PSM are comparatively better than other conventional methods. The rest of this chapter is organized as follows: Section 9.2 brie y introduces the near-infrared face recognition and describes the fusion of NIR face with VL face and the fusion of NIR face with iris modality respectively. Section 9.3 describes several fusion methods. The experimental results and discussions are presented in Section 9.4, and in Section 9.5 we conclude the chapter.
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