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.

1. Discriminative Learning in Biometrics.- 2. Metric Learning with Biometric Applications.- 3. Sparse Representation-based Classification for Biometric Recognition.- 4. Discriminative Features for Palmprint Authentication.- 5. Orientation Features and Distance Measure of Palmprint Authentication.- 6. Multifeature Palmprint Authentication.- 7. Discriminative Learning via Encouraging Virtual Face Images.- 8. Sparse Representation-based Methods for Face Recognition.- 9. Fusion Methodologies of Multiple Traits.- 10. Discussions and Future Work.