Palm-vein classification based on principal orientation features.
Personal recognition using palm-vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm-vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm-vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm-vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm-vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm-vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database.
School of biomedical engineering, Southern Medical University, Guangzhou 510515, China.
Yujia
Y
Zhou
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, 61820, United States of America.
Yaqin
Y
Liu
School of biomedical engineering, Southern Medical University, Guangzhou 510515, China.
Qianjin
Q
Feng
School of biomedical engineering, Southern Medical University, Guangzhou 510515, China.
Feng
F
Yang
School of biomedical engineering, Southern Medical University, Guangzhou 510515, China.
Jing
J
Huang
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, 61820, United States of America.
Yixiao
Y
Nie
D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D003878:Dermatoglyphics / Q000145:classification*; D006801:Humans