Face Recognition: Issues, Methods and Alternative Applications

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition

Face Recognition Through Regret Minimization.

Face Recognition is an important problem for Artificial Intelligence Researchers, with applications to law enforcement, medicine and entertainment. Many different approaches to the problem have been suggested most approaches can be categorized as being either Holistic or Local. Recently, local approaches have shown some gains. This thesis presents a system for embedding a holistic algorithm into a local framework. The system proposed builds on the concept of Regional Voting, to create Weighted Regional Voting which divides the face images to be classified into regions, performs classification on each region, and finds the final classification through a weighted majority vote on the regions. Three different weighting schemes taken from the field of Regret Minimization are suggested, and their results compared. Weighted Regional Voting is shown to improve upon unweighted Regional Voting in every case, and to outperform or equal many modern face recognition algorithms. --P. ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b174112

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity

Regional displacement matching scheme for LBP based face recognition.

In face recognition, alignment of the face images has been a known open issue. This thesis proposes a displacement based local aligning scheme to construct a structural descriptive image template for comparison. To conquer the registration difficulties caused by the non-rigidity of human face images, a block displacement strategy is introduced to apply the regional voting scheme to face recognition field. Local Binary Pattern (LBP) is adopted to construct this block LBP displacement-based local matching approach, we name LBP-DLMA. Experiments are performed and have demonstrated the outstanding performances of this LBP-DLMA over the original LBP approach. It is expected and shown by experiments that this approach applies to both large and small sized images, and that it also applies to descriptor approaches other than LBP. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b189084

NBS monograph

From Introduction: "This report is the first of a series intended to provide a selective overview of research and development efforts and requirements in the somewhat overlapping fields of the computer and information sciences and technologies. The projected series of reports will attempt to outline the probable range of R & D activities in the computer and information sciences and technologies through selective reviews of the literature and to develop a reasonable consensus with respect to the opinions of workers in these and potentially related fields as to areas of continuing R & D concern for research program planning or review in these areas.