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

Iodine

书目名称Discriminative Learning in Biometrics影响因子(影响力)




书目名称Discriminative Learning in Biometrics影响因子(影响力)学科排名




书目名称Discriminative Learning in Biometrics网络公开度




书目名称Discriminative Learning in Biometrics网络公开度学科排名




书目名称Discriminative Learning in Biometrics被引频次




书目名称Discriminative Learning in Biometrics被引频次学科排名




书目名称Discriminative Learning in Biometrics年度引用




书目名称Discriminative Learning in Biometrics年度引用学科排名




书目名称Discriminative Learning in Biometrics读者反馈




书目名称Discriminative Learning in Biometrics读者反馈学科排名

LAIR

条约

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.

上下连贯

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.

讥笑

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.

讥笑

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.

冲突

委屈

HEED

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.