Discriminative Learning in Biometrics
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Beschreibung
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.
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.
Summarizes the latest studies on discriminative learning methods and their applications to biometric recognition Covers different biometric recognition technologies, including face recognition, palmprint authentication, and multi-biometrics Provides numerous executive procedures in addition to text presentation of the novel algorithms, technical schemes and insightful strategies Written by one of the 2014 ISI Highly Cited Researchers and pioneers of biometrics research Includes supplementary material: sn.pub/extras
Autor*in
David Zhang
Themen in »Discriminative Learning in Biometrics«
Biometrics Discriminative learning Palmprint authentication Face recognition Multi-biometrics Pattern recognition Feature extraction Sparse representation Metric learning