Regularized Independent Component Analysis in Face Verification Pang Ying Han 1 , Teo Chuan Chin 1* , Ooi Shih Yin 1 , Lau Siong Hoe 1 , Hiew Fu San 2 , Liew Yee Ping 1 1 Faculty of Information Science and Technology, Multimedia University, 75450 Melaka, Malaysia 2 Infineon Technologies (Malaysia) Sdn. Bhd., Free Trade Zone, Batu Berendam, 75450 Melaka, Malaysia yhpang@mmu.edu.my; ccteo@mmu.edu.my * ; syooi@mmu.edu.my; lau.siong.hoe@mmu.edu.my ABSTRACT A regularized Independent Component Analysis (denoted as RICA) is proposed in the application of face verification. In RICA, information of correlation coefficients between images is employed to form a Laplacian matrix. This Laplacian matrix is used for locating localized features through regularizing the facial data before independent component analysis (ICA) feature extraction. Since there are two different architectures of ICA (ICA I and ICA II), RICA is implemented on these two architectures, namely RICA_ICA I and RICA_ICA II, respectively. Two face datasets are adopted to access the effectiveness of the proposed techniques. The databases are Facial Recognition Technology (FERET) and CMU Pose, Illumination, and Expression (CMU PIE). From the experimental results, it is demonstrated that the both proposed techniques, RICA_ICA I and RICA_ICA II, are able to show its superiority in face verification. KEYWORDS Face; correlation coefficients; Laplacian matrix; regularization; Independent Component Analysis. 1 INTRODUCTION In pattern recognition, the captured image data is usually represented in very high dimension. Noise and redundant information are embedded in this high dimensional form, leading to performance degradation. This is known as curse of dimensionality. Hence, numbers of facial feature extraction techniques have been researched and introduced in order to transform/ project this high dimensional data into a more compact but informative feature representation [1][2][3][4][5][6]. Principal Component Analysis (PCA) is one of the well-known feature extraction techniques in pattern recognition, especially in face recognition [1]. In PCA, data variance is maximized so that the uncorrelated coefficients of the data could be visible for image representation. However, the robustness of this technique is constrained by its unsupervised learning nature. Therefore, this technique is then further enhanced through incorporating supervised learning mode for better performance by Belhumeur et al. [2]. This supervised technique is known as Linear Discriminant Analysis (LDA). From the perspective of pattern recognition, higher-order dependencies in an image usually comprise nonlinear relations among pixels. And, this nonlinear information is significant for recognition. Hence, Independent Component Analysis (ICA) is proposed [3]. ICA attempts to seek a set of basis signals that are statistically independent. From the literature, two different architectures of ICA implementation are proposed. ICA architecture I (denoted as ICA I) treats images as random variables and pixels as outcomes; and, ICA architecture II (denoted as ICA II) treats image pixels as random variables and images as outcomes [3]. It is claimed that ICA I is focused on spatially localized features, whereas ICA II is mainly focused on global features [3]. The superiority of these two architectures has been demonstrated by Yuen and Lai [7] as well as Steward [3] in their research works. In literature, there is a relatively new research path for discriminative data learning, that is based on the improvement of population statistics estimation and data locality preservation [8][9][10]. In feature analysis, training data is important for feature extraction techniques to ISBN: 978-0-9891305-2-3 ©2013 SDIWC 60