Robust Face Image Matching under Illumination Variations
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Robust Face Image Matching under Illumination Variations Chyuan-Huei Thomas Yang Department of Computer Science, National Tsing Hua University, 101 Kuang Fu Road, Section 2, Hsinchu 300, Taiwan Email: [email protected]
Shang-Hong Lai Department of Computer Science, National Tsing Hua University, 101 Kuang Fu Road, Section 2, Hsinchu 300, Taiwan Email: [email protected]
Long-Wen Chang Department of Computer Science, National Tsing Hua University, 101 Kuang Fu Road, Section 2, Hsinchu 300, Taiwan Email: [email protected] Received 1 September 2003; Revised 21 September 2004 Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions. Keywords and phrases: robust image matching, face recognition, illumination variations, normalized gradient.
1. INTRODUCTION Face recognition has attracted the attention of a number of researchers from academia and industry because of its challenges and related applications, such as security access control, personal ID verification, e-commerce, video surveillance, and so forth. The details of these applications are referred to in the surveys [1, 2, 3]. Face matching is the most important and crucial component in face recognition. Although there have been many efforts in previous works to achieve robust face matching under a wide variety of different image capturing conditions, such as lighting changes, head pose or view angle variations, expression variations, and so forth, these problems are still difficult to overcome. It is a g
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