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Illumination Invariant Face Recognition Using Near-Infrared Images
April 2007 (vol. 29 no. 4)
pp. 627-639
Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups.
Index Terms:
Biometrics, face recognition, near infrared (NIR), illumination invariant, local binary pattern (LBP), statistical learning.
Citation:
Stan Z. Li, RuFeng Chu, ShengCai Liao, Lun Zhang, "Illumination Invariant Face Recognition Using Near-Infrared Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 627-639, April 2007, doi:10.1109/TPAMI.2007.1014
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