Cross-Spectral Full and Partial Face Recognition: Preprocessing, Feature Extraction and Matching

Cross-spectral face recognition remains a challenge in the area of biometrics. The problem arises from some real-world application scenarios such as surveillance at night time or in harsh environments, where traditional face recognition techniques are not suitable or limited due to usage of imagery obtained in the visible light spectrum. This motivates the study conducted in the dissertation which focuses on matching infrared facial images against visible light images. The study outspreads from aspects of face recognition such as preprocessing to feature extraction and to matching.;We address the problem of cross-spectral face recognition by proposing several new operators and algorithms based on advanced concepts such as composite operators, multi-level data fusion, image quality parity, and levels of measurement. To be specific, we experiment and fuse several popular individual operators to construct a higher-performed compound operator named GWLH which exhibits complementary advantages of involved individual operators. We also combine a Gaussian function with LBP, generalized LBP, WLD and/or HOG and modify them into multi-lobe operators with smoothed neighborhood to have a new type of operators named Composite Multi-Lobe Descriptors. We further design a novel operator termed Gabor Multi-Levels of Measurement based on the theory of levels of measurements, which benefits from taking into consideration the complementary edge and feature information at different levels of measurements.;The issue of image quality disparity is also studied in the dissertation due to its common occurrence in cross-spectral face recognition tasks. By bringing the quality of heterogeneous imagery closer to each other, we successfully achieve an improvement in the recognition performance. We further study the problem of cross-spectral recognition using partial face since it is also a common problem in practical usage. We begin with matching heterogeneous periocular regions and generalize the topic by considering all three facial regions defined in both a characteristic way and a mixture way.;In the experiments we employ datasets which include all the sub-bands within the infrared spectrum: near-infrared, short-wave infrared, mid-wave infrared, and long-wave infrared. Different standoff distances varying from short to intermediate and long are considered too. Our methods are compared with other popular or state-of-the-art methods and are proven to be advantageous

Human face recognition under degraded conditions

Comparative studies on the state of the art feature extraction and classification techniques for human face recognition under low resolution problem, are proposed in this work. Also, the effect of applying resolution enhancement, using interpolation techniques, is evaluated. A gradient-based illumination insensitive preprocessing technique is proposed using the ratio between the gradient magnitude and the current intensity level of image which is insensitive against severe level of lighting effect. Also, a combination of multi-scale Weber analysis and enhanced DD-DT-CWT is demonstrated to have a noticeable stability versus illumination variation. Moreover, utilization of the illumination insensitive image descriptors on the preprocessed image leads to further robustness against lighting effect. The proposed block-based face analysis decreases the effect of occlusion by devoting different weights to the image subblocks, according to their discrimination power, in the score or decision level fusion. In addition, a hierarchical structure of global and block-based techniques is proposed to improve the recognition accuracy when different image degraded conditions occur. Complementary performance of global and local techniques leads to considerable improvement in the face recognition accuracy. Effectiveness of the proposed algorithms are evaluated on Extended Yale B, AR, CMU Multi-PIE, LFW, FERET and FRGC databases with large number of images under different degradation conditions. The experimental results show an improved performance under poor illumination, facial expression and, occluded images

Heterogeneous Techniques used in Face Recognition: A Survey

Face Recognition has become one of the important areas of research in computer vision. Human Communication is a combination of both verbal and non-verbal. For interaction in the society, face serve as the primary canvas used to express distinct emotions non-verbally. The face of one person provides the most important natural means of communication. In this paper, we will discuss the various works done in the area of face recognition where focus is on intelligent approaches like PCA, LDA, DFLD, SVD, GA etc. In the current trend, combination of these existing techniques are being taken into consideration and are discussed in this paper.Keywords: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Genetic Algorithm (GA), Direct Fractional LDA (DFLD

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed