Review of palmprint biometric recognition systems and their feasibility

Authors

  • Srushti Kureel,Sourabh Sharma

Keywords:

Image Processing, biometric, texture, recognition, palmprint, fingerprint, authentication, Collectability, extraction

Abstract

With the development of more and more systems which provide service based on the identity of a person, the importance of personal identification is growing. Providing authorized users with secure access to the services is a challenge to the personal identification systems. There are several conventional means for personal identification which include passports, keys, tokens, access cards, personal identification number (PIN), passwords. Unfortunately, passports, keys, access cards, tokens, can be lost, stolen or duplicated, and passwords, PINs can be forgotten, cracked or shared. These drawbacks cause a great loss to the concerned. Biometric systems are proving to be an efficient solution to this problem.

A biometric identity verification system tries to verify user identities by comparing some sort of behavioural or physiological trait of the user to a previously stored sample of the trait. The recent developments in the biometrics area have lead to smaller, faster and cheaper systems, which in turn has increased the number of possible application areas for biometric identity verification. Palmprint can be one of the biometrics, used for personal identification or verification. As a small central part of the palmprint image is used for this purpose, so it is important to find that region of interest.

In this paper, review of the palmprint analysis and the methodology used in the literature has been discussed. Various biometric has been critically analysed and way of choosing the applicable methods has been discussed.

References

N. Frykholm, Passwords: Beyond the Terminal Interaction Model, University of Umea, 2000.

G. A. Miller, “The magical number seven, plus or minus two: some limits on our capacity for processing information,” Psychological Review, vol. 63, pp. 81–97, 1956.

Salil Prabhakar, Anil K. Jain, and Lin Hong, “A multichannel approach to fingerprint classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 348–359, 1999.

Salil Prabhakar, Sharath Pankanti, and Anil K. Jain, “On the individuality of fingerprints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1010–1025, 2002.

D. Zhang and W. Shu, “Two novel characteristics in palmprint verification: Datum point invariance and line feature matching,” Pattern Recognition, vol. 32, no. 4, pp. 691–702, 1999.

David Zhang, Wenxin Li, and Zhuoqun Xu, “Palmprint identification by Fourier transform,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 16, no. 4, pp. 417–432, 2002.

Ding Tianhuai, Su Xiaosheng, and Lin Xirong, “Palmprint feature extraction based on wavelet transform,” Tsinghua Univ (Sci and Tech), vol. 43, no. 8, pp. 1049–1051, 1055, 2003.

K. C. Fan, C. C. Han, H. L. Cheng, and C. L. Lin, “Personal authentication using palmprint features,” Pattern Recognition (Special issue: Biometrics), vol. 36, no. 2, pp. 371–381, 2003.

David Zhang, Wai Kin Kong, and Wenxin Li, “Palmprint feature extraction using 2-D Gabor filters,” Pattern Recognition, vol. 36, pp. 2339–2347, 2003.

Junying Gan and Dangpei Zhou, “A novel method for palmprint recognition based on wavelet transform,” in Proc. IEEE International Conference on Signal Processing (ICSP2006), vol. 3, 2006.

Kuanquan Wang, Xiangqian Wu, and David Zhang, “Palmprint texture analysis using derivative of Gaussian filters,” in Proc. IEEE International Conference on Computational Intelligence and Security (ICCIS2006), vol. 1, pp. 751–754, 2006.

Michael M. Bronstein, Alexander M. Bronstein, and Ron Kimmel, “Three dimensional face recognition,” International Journal of Computer Vision, vol. 64, no. 1, pp. 5–30, 2005.

Yossi Zana and Jr. Roberto M. Cesar, “Face recognition based on polar frequency features,” ACM Transactions on Applied Perception, vol. 3, no. 1, pp. 62–82, 2006.

BOUCHEMHA Amel and DOGHMANE Nourreddine, “Level Feature Fusion of Multispectral Palmprint Recognition using the Ridgelet Transform and OAO Multi-class Classifier,” IEEE, 978-1-4673-5200-0/13, 2013.

Xin Wu, Zhigang Zhao, Danfeng Hong, Weizhong Zhang, Zhenkuan Pan, and Jiaona Wan, “A Palmprint Recognition Algorithm Based on Binary Horizontal Gradient Orientation and Local Information Intensity,” IEEE, 978-1-4799-2565-0/13, 2013.

Ruifang Wang, Daniel Ramos, Julian Fierrez, and Ram P. Krish, “Automatic Region Segmentation for High-resolution Palmprint Recognition: Towards Forensic Scenarios,” IEEE, 2014.

Faegheh Shojaiee and Farshid Hajati, “Local Composition Derivative Pattern for Palmprint Recognition,” IEEE, 978-1-4799-4409-5/14, 2014.

Shervin Minaee and Amir Ali Abdolrashidi, “Multispectral Palmprint Recognition Using Textural Features,” IEEE, 2014.

R. Raghavendra and Christoph Busch, “Texture based features for robust palmprint recognition: a comparative study,” Springer, 2015.

Downloads

How to Cite

Srushti Kureel,Sourabh Sharma. (2025). Review of palmprint biometric recognition systems and their feasibility . International Journal of Research & Technology, 4(3), 10–16. Retrieved from https://ijrt.org/j/article/view/46