Illumination Invariant Face Recognition Using Near-Infrared Images

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 4; pp. 627 - 639
• Main Authors: Li, S.Z., RuFeng Chu, ShengCai Liao, Lun Zhang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biometrics; Biometry - methods; Computer Simulation; Engines; Face - physiology; Face detection; Face recognition; Feature extraction; Hardware; Humans; Illumination; illumination invariant; Imaging; Indoor; Infrared imaging; Invariants; Lighting; Lighting - methods; local binary pattern (LBP); Models, Biological; near infrared (NIR); Optical imaging; Optical reflection; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Skin Physiological Phenomena; Spectrophotometry, Infrared - methods; Statistical learning; Thermography - methods; Transforms
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2007.1014

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