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

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書目名稱Discriminative Learning in Biometrics影響因子(影響力)




書目名稱Discriminative Learning in Biometrics影響因子(影響力)學(xué)科排名




書目名稱Discriminative Learning in Biometrics網(wǎng)絡(luò)公開度




書目名稱Discriminative Learning in Biometrics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discriminative Learning in Biometrics被引頻次




書目名稱Discriminative Learning in Biometrics被引頻次學(xué)科排名




書目名稱Discriminative Learning in Biometrics年度引用




書目名稱Discriminative Learning in Biometrics年度引用學(xué)科排名




書目名稱Discriminative Learning in Biometrics讀者反饋




書目名稱Discriminative Learning in Biometrics讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-981-97-3629-4 to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importance in the sense that it forms the foundation for much of the subsequent parts in this book.

上下連貫

https://doi.org/10.1007/978-981-19-4859-6on, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give a brief introduction to the representation by dictionary learning algorithm. Based on the sparse representation, we present a novel multiple representations for image classification.

Estrogen

Ecology and Sustainable Development in Japane a brief review of palmprint authentication methods in Sect.?.. Section?. describes the conventional coding-based palmprint identification methods. In Sects.?. and ., two improved coding-based palmprint authentication methods are presented.

譏笑

Xuan Lam Nguyen,Kaliappa Kalirajanhese possible changes, so it is hard to obtain very high accuracy for real-world face recognition. In this chapter, we present some effective schemes based on competent virtual face images to overcome the above problems. The adopted schemes and algorithms also seem to be applicable for some other applications.

譏笑

Karembe F. Ahimbisibwe,Tiina Kontinenlying rationales are also provided for the better understanding of these methods. In this chapter, we will give a further discussion about the book and present some remarks on the future development of discriminative learning for biometric recognition.

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Sparse Representation-Based Classification for Biometric Recognitionon, segmentation, classification, and visual tracking. In this chapter, we first summarize some frameworks of sparse representation, and then we give a brief introduction to the representation by dictionary learning algorithm. Based on the sparse representation, we present a novel multiple representations for image classification.