Illumination Invariant Face Recognition Using Near-Infrared Images

Abstract

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.

Millimetre wave person recognition: Hand-crafted vs learned features

“…1) Local Binary Patterns features: Local Binary Patterns provide discriminatory texture information that has been proved to be robust against illumination variations [7]. In this case, first images are resized to 100 × 150 (width × height format).…”

Template based Mole Detection for Face Recognition

“…This includes prototypical 3D facial expression shapes and 2D facial textures of 2500 models from 100 subjects to solve the problems inherent in the 2D based analysis. Stan Z et al, [18] presented illumination invariant face recognition system for indoor, cooperative person using active near infrared imaging hardware techniques. The AdaBoost procedure is used to learn face recognition based on the invariant representation.…”

Outdoor Face Recognition Using Enhanced Near Infrared Imaging

“…After that, an ENIR face recognition system is built. Experimental results show that the system performs slightly better than the existing NIR system [8,9] while both are significantly better than visible light image based systems. In outdoor sunlight, the ENIR system can achieve relatively high recognition rate while NIR and visible light (VIS) systems cannot function properly.…”

“…In particular, infrared (IR) imaging sensor has become an area of growing interest [7]. Near-infrared (NIR) image based techniques have been developed to achieve highly accurate face recognition for indoor applications under varying lighting conditions [8,9].…”