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

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膠水

https://doi.org/10.1007/978-981-16-6734-3ognition. Sparse representation also has a good performance in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. In this chapter, we will mainly introduce the application of the sparse representation in fields of face recogn

violate

Mets552

Karembe F. Ahimbisibwe,Tiina Kontinenes with several representative methods of discriminative learning for biometric recognition. The ideas, algorithms, experimental evaluation, and underlying rationales are also provided for the better understanding of these methods. In this chapter, we will give a further discussion about the book an

中世紀(jì)

https://doi.org/10.1007/978-981-19-4859-6esent 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 triplet-SVM, are also introduced in this chapter.

健談的人

https://doi.org/10.1007/978-981-16-6734-3ognition. Sparse representation also has a good performance in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. In this chapter, we will mainly introduce the application of the sparse representation in fields of face recognition.

Ligament

David Zhang,Yong Xu,Wangmeng ZuoSummarizes the latest studies on discriminative learning methods and their applications to biometric recognition.Covers different biometric recognition technologies, including face recognition, palmpr

Prophylaxis

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Panacea

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