A novel chaotic neural network K-set has been constructed based in research on biological olfactory systems. This non-convergent neural network simulates the capacities of biological brains for signal processing in pattern recognition. Its accuracy and efficiency are demonstrated in this report on an application to human face recognition, with comparisons of performance with conventional pattern recognition algorithms.

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 conduct‐ ed 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 verifica‐ tion 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 build‐ ing 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 recogni‐ tion methods and applications and their potential use in areas not related to face recognition.

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Face Recognition: Issues, Methods and Alternative Applications

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Face Recognition Using a Neural Network Simulating Olfactory Systems - eScholarship

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Face Recognition Using a Neural Network Simulating Olfactory Systems - eScholarship

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