Local binary feature learning has attracted a lot of researches in image recognition due to its vital effectiveness. Generally, in the traditional local feature learning methods, a projection is learned to map the patches of image into binary features and then a codebook is generated by clustering the binary features with K-means clustering. However, these local feature learning methods, such as compact binary face descriptor and discriminative binary descriptor, ignore the category specific distributions of the original features during the feature learning process and use the real-valued clustering approach to generate the codebook, the discriminant of feature is degraded and the merits of binary feature are lost. To tack these problems, in this paper, we propose a novel category-preserving binary feature learning and binary codebook leaning (CPBFL-BCL) method for finger vein recognition. In CPBFL-BCL, the discrimination of learned binary features is generated by criteria of fisher discriminant analysis and category manifold preserving regularity during the feature learning process, and a novel binary clustering method based on K-means clustering is designed to generate binary codebook. Experimental results on recognition and retrieval tasks using two public finger vein databases are presented and demonstrate the effectiveness and efficiency of the proposed method over the state-of-the-art finger vein methods and a finger vein retrieval method.

局部二值特征学习由于其至关重要的作用而吸引了很多图像识别方面的研究。通常，在传统的局部特征学习方法中，学习投影以将图像块映射为二进制特征，然后通过将二进制特征与K聚类来生成码本。-表示聚类。但是，这些局部特征学习方法（例如紧凑型二进制人脸描述符和区分性二进制描述符）在特征学习过程中会忽略原始特征的类别特定分布，并使用实值聚类方法生成码本（特征的判别式）被降级，二元特征的优点消失了。为了解决这些问题，在本文中，我们提出了一种新颖的保留类别的二进制特征学习和二进制密码本学习（CPBFL-BCL）方法，用于手指静脉识别。在CPBFL-BCL中，特征学习过程中的费舍尔判别分析和类别流形保持规律性的判据产生了对学习的二元特征的判别，并提出了一种新的基于K的二元聚类方法。-means聚类旨在生成二进制码本。提出了使用两个公共手指静脉数据库进行识别和检索任务的实验结果，并证明了该方法相对于最新的手指静脉方法和手指静脉检索方法的有效性和效率。