This monograph describes the latest advances in discriminative
learning methods for biometric recognition. Specifically, it
focuses on three representative categories of methods: sparse
representation-based classification, metric learning, and
discriminative feature representation, together with their
applications in palmprint authentication, face recognition and
multi-biometrics. The ideas, algorithms, experimental evaluation
and underlying rationales are also provided for a better
understanding of these methods. Lastly, it discusses several
promising research directions in the field of discriminative
biometric recognition.
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