Review of palmprint biometric recognition systems and their feasibility
Keywords:
Image Processing, biometric, texture, recognition, palmprintAbstract
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, 2000. University of Umea.
Miller G. A., “The magical number seven, plus or minus two: some limits on our capacity for processing information”, Psychological Review, 63:81–97, 1956.
Salil Prabhakar, Anil K. Jain and Lin Hong, “A multichannel approach to fingerprint classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):348–359, 1999.
Salil Prabhakar, Sharath Pankanti and Anil K. Jain, “On the individuality of fingerprints”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8):1010–1025, 2002.
D. Zhang and W. Shu, “Two novel characteristics in palmprint verification: Datum point invariance and line feature matching”, Pattern Recognition, 32(4):691–702, 1999.
David Zhang, Wenxin Li and Zhuoqun Xu, “Palmprint identification by fourier transform”, International Journal of Pattern Recognition and Artificial Intelligence, 16(4):417–432, 2002.
Ding Tianhuai, Su Xiaosheng, Lin Xirong, “Palmprint feature extraction based on wavelet transform”, Tsinghua Univ (Sci and Tech), 43(8):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), 36(2):371–381, 2003.
David Zhang, Wai Kin Kong and Wenxin Li, “Palmprint feature extraction using 2-D gabor filters”, Pattern Recognition, 36:2339–2347, 2003.
Jun ying Gan and Dang pei Zhou, “A novel method for palmprint recognition based on wavelet transform”, IEEE Proceedings of International Conference on Signal Processing (ICSP2006), volume 3, 2006.
Kuanquan Wang, Xiangqian Wu and David Zhang, “Palmprint texture analysis using derivative of gaussian filters”, in The IEEE Proceedings of International Conference on Computational Intelligence and Security (ICCIS2006), volume 1, pages 751–754, 2006.
Michael M. Bronstein, Alexander M. Bronstein and Ron Kimmel, “Three dimensional face recognition”, International Journal of Computer Vision, 64(1):5–30, 2005.
Yossi Zana and Jr Roberto M. Cesar, “Face recognition based on polar frequency features”, ACM Transactions on Applied Perception, 3(1):62–82, 2006.
Medha Misar, Damayanti Gharpure, “Extraction of Feature Vector Based on Wavelet Coefficients for a Palm Print based Biometric Identification System”, Proceedings of the 2015 2nd International Symposium on Physics and Technology of Sensors, 8-10th March, 2015, Pune, India.
Gaurav Jaswal, Ravinder Nath, Amit Kaul, “Texture based Palm Print Recognition using 2-D Gabor Filter and Sub Space Approaches”, 2015 International Conference on Signal Processing, Computing and Control (2015 ISPCC).
M. L. Anitha, K.A. Radhakrishna Rao, “An Efficient Approach for Classification of Palmprint images using Heart Line Features”, International Conference on Emerging Research in Electronics, Computer Science and Technology – 2015.
Aishwarya D, Gowri M, Saranya R K, “Palm Print Recognition Using Liveness Detection Technique”, 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM).
Saranraj S, Padmapriya V, Sudharsan S, Piruthiha D and Venkateswaran N, “Palm Print Biometric Recognition based on Scattering Wavelet Transform”, 978-1-4673-9338-6, IEEE WiSPNET 2016 Conference.
Afsal. S, Rafeeq Ahamed. K, Jijo Jothykumar, Shabeer Ahmed, Farrukh Sayeed, “A Novel Approach for Palm print Recognition using Entropy Information Features”, 978-1-4673-9338-6, IEEE WiSPNET 2016 Conference.
Shivkant Kaushik, Rajendra Singh, “A New Hybrid Approch for Palm Print Recognition in PCA Based Palm Print Recognition System”, 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Sep. 7-9, 2016, AIIT, Amity University Uttar Pradesh, Noida, India.
R. Raghavendra and Christoph Busch, “Texture based features for robust palmprint recognition: a comparative study”, Springer, 2015.
Bouchemha Amel, Doghmane Nourreddine, “Level Feature Fusion of Multispectral Palmprint Recognition using the Ridgelet Transform and OAO Multi-class Classifier”, 978-1-4673-5200-0/13, IEEE, 2013.
Xin Wu, Zhigang Zhao, Danfeng Hong, Weizhong Zhang, Zhenkuan Pan, Jiaona Wan, “A Palmprint Recognition Algorithm Based On Binary Horizontal Gradient Orientation and Local Information Intensity”, 978-1-4799-2565-0/13, IEEE, 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, Farshid Hajati, “Local Composition Derivative Pattern for Palmprint Recognition”, 978-1-4799-4409-5/14, IEEE, 2014.
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