458 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 2, APRIL 2006 Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain Weilong Chen, Meng Joo Er, Member, IEEE, and Shiqian Wu, Member, IEEE Abstract—This paper presents a novel illumination normalization ap- proach for face recognition under varying lighting conditions. In the pro- posed approach, a discrete cosine transform (DCT) is employed to compen- sate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. More- over, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system. Index Terms—Discrete cosine transform, face recognition, illumination normalization, logarithm transform. I. INTRODUCTION Face recognition has attracted significant attention because of its wide range of applications [1]. Recently, more researchers focus on robust face recognition such as face recognition systems invariant to pose, expression and illumination variations. Illumination variation is still a challenging problem in face recognition research area, especially for appearance-based approaches. The same person can appear greatly different under varying lighting conditions. A variety of approaches have been proposed to solve the problem [3]–[16]. These approaches can be generally classified into three main categories. • Preprocessing and Normalization: In this approach, face images are preprocessed using some image processing tech- niques to normalize the images to appear stable under different lighting conditions. For instance, histogram equalization (HE), Gamma correction, logarithm transform, etc. are widely used for illumination normalization [3], [4]. However, nonuniform illumination variation is still difficult to deal with using these global processing techniques. Recently, adaptive histogram equalization (AHE) [2], region-based histogram equalization (RHE) [3], and block-based histogram equalization (BHE) [5] have also been proposed to cope with nonuniform illumination variations. Although recognition rates on face databases with nonuniform illumination variations can be improved compared with the HE, their performances are still not satisfactory. In [13], by combining symmetric shape-from-shading (SSFS) and a generic three-dimensional (3-D) model, the performance of face recognition under varying illuminations is enhanced. However, this method is only efficient for exact frontal face images and it is assumed that all faces share a similar common Manuscript received October 16, 2004; revised March 9, 2005. This paper was recommended by Associate Editor Maja Pantic. W. Chen is with the Computer Control Lab, Nanyang Technological Univer- sity, SIngapore 639798. M. J. Er is with the Intelligent Systems Centre, Nanyang Technological Uni- versity, Singapore 637533 (e-mail: EMJER@ntu.edu.sg). S. Wu is with the Institute for Inforcomm Research, Nanyang Technological University, Singapore 119613. Digital Object Identifier 10.1109/TSMCB.2005.857353 shape. In [3], the authors proposed a normalization method called quotient illumination relighting (QIR). This method is based on the assumption that the lighting modes of the images are known or can be estimated. • Invariant Feature Extraction: This approach attempts to ex- tract facial features which are invariant to illumination varia- tions. Edge maps, derivatives of the gray-level and Gabor-like filters are investigated in [9]. However, empirical studies show that none of these representations are sufficient to overcome image variations due to changes in the direction of illumination. Another well-known feature extraction method is called Fisher- face [also known as linear discriminant analysis (LDA)] which linearly projects the image space to a low-dimensional subspace to discount variations in lighting and facial expressions [11]. But, this method is a statistical linear projection method which largely relies on representativeness of the training samples. In [12], the quotient image is regarded as the illumination invariant signature image which can be used for face recognition under varying lighting conditions. Bootstrap database is required for this method and the performance degrades when dominant fea- tures between the bootstrap set and the test set are misaligned. • Face Modeling: Illumination variations are mainly due to the 3-D shape of human faces under lighting in different directions. Recently, some researchers attempt to construct a generative 3-D face model that can be used to render face images with different poses and under varying lighting conditions [6], [7], [10] and [14]. A generative model called illumination cone was presented in [6], [7]. The main idea of this method is that the set of face images in fixed pose but under different illumination conditions can be represented using an illumination convex cone which can be constructed from a number of images acquired under variable lighting conditions and the illumination cone can be approxi- mated in a low-dimensional linear subspace. In [10], the authors showed that the set of images of a convex Lambertian object obtained under a variety of lighting conditions can be well ap- proximated by a 9D linear subspace. One of the drawbacks of the model-based approaches is that a number of images of the subject under varying lighting conditions or 3-D shape informa- tion are needed during the training phase. This drawback limits its applications in practical face recognition systems. In addi- tion, existing model-based approaches assume that the human face is a convex object, i.e., the casting shadows are not consid- ered. The specularity problem is also ignored even though the human face is not a perfect Lambertian surface. To the best of our knowledge, one ideal way of solving the illumina- tion variation problem is to normalize a face image to a standard form under uniform lighting conditions. In fact, the human visual system usually cares about the main features of a face, such as the shapes and relative positions of the main facial features, and ignores illumination changes on the face while recognizing a person. Accordingly, in this paper, we propose an illumination normalization approach to remove illumination variations while keeping the main facial features unim- paired. The key idea of the proposed approach is that illumination vari- ations can be significantly reduced by truncating low-frequency dis- crete cosine transform (DCT) coefficients in the logarithm DCT do- main. Our approach can be categorized into the first approach group although feature extraction can be carried out directly in the logarithm DCT domain. 1083-4419/$20.00 © 2006 IEEE