Titlebook: Discriminative Learning in Biometrics; David Zhang,Yong Xu,Wangmeng Zuo Book 2016 Springer Science+Business Media Singapore 2016 Biometric

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Discriminative Learning in Biometricsirst give an overview on the systems in terms of the input features and common applications. After that, we will provide a self-contained introduction to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importanc

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Metric Learning with Biometric Applicationsesent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the trip

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Sparse Representation-Based Classification for Biometric Recognitionthod has received much attention in recent years and is widely applied in many fields, such as image denoising, debluring, restoration, super-resolution, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give

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Discriminative Features for Palmprint Authentications, which extract the coding features of palmprint images, are among the most promising palmprint authentication methods. In this chapter, we first give a brief review of palmprint authentication methods in Sect. .. Section . describes the conventional coding-based palmprint identification methods. I

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Orientation Features and Distance Measure of Palmprint AuthenticationFor the orientation code-based methods, the orientation extraction and distance measure are two essential issues for palmprint verification. In this chapter, some efficient orientation extraction methods and a novel distance measure method are presented. The chapter is organized as follows. We first

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