Adaptive Linear Gabor Response Pattern for Palm-print Recognition
Abstract
The advancement of the information society has increased the demand for secure identity systems in a wide range of applications, including financial transactions, forensics, border control, computer security, and law enforcement. Biometrics is an alternative method for accurate and trustworthy authentication in a highly networked society. It is the science of using behavioural (gait, keystroke, signature, etc.) and physiological (face, ear, finger, palmprint, DNA etc.) attributes to validate an individual's identity. The special attributes on which the biometric recognition system is developed are known as modalities or identifiers. Recently, research in hand biometrics has shifted to examining the value of palm-prints in real-world contexts such as smartphones, ATMs, and notebooks. Palm prints are one of the most reliable biometrics available. The palm-print is considered as a bit of texture and texture-based feature extraction method has been presented during this work. This work proposes a palm-print recognition system that extracts textural characteristics by adjusting the optimum Gabor filter settings, named as adaptive linear Gabor response pattern (ALGRP). A distinct Gabor filter orientation is used to create a histogram for each sub-block of an image, and the sub-blocks of images are then concatenated to create a feature vector. The matching was made translation and rotation-invariant by using the normalized hamming distance. The proficiency of the proposed technique has been assessed on three publicly available palm-print databases. Test results exhibited that the method accomplished good performance with an EER of 0.094% and 0.13% on PolyU and IIT-Delhi databases respectively. We show that the proposed method outperforms the existing ones in terms of performance.
Keywords
Full Text:
PDFReferences
Jain A.K., Ross A., Prabhakar S., An introduction to biometric recognition, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 4-20.
Zhang S., and Gu X., Palmprint recognition based on the representation in the feature space, Optik, 2013, 124, pp. 5434-5439.
Jing Li, Jian Cao and Kaixuan Lu, Improve the two-phase test samples representation method for palmprint recognition, Optik, 2013, 1124, (24), pp. 6651-6656.
You J., Li W.X., and Zhang D, Hierarchical palmprint identification via multiple feature extraction, Pattern Recognit., 2002, 35, pp. 847–859.
Kong W.K. and Zhang D., Competitive coding scheme for palm print verification, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 520–523.
Zhang D., Kong W.K., You J. et al., On-line palmprint identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25, (9), pp. 1041-1050.
Diaz M.R., Travieso C.M., Alonso J. B. et al., Biometric system based in the feature of hand palm, Proc. Int. Carnahan Conf. on Security Technology, 2004, pp. 136–139.
Wu X. Q., and Zhao Q. S., Deformed palmprint matching based on stable regions, IEEE Trans. Image Process., 2015, 24, (2), pp. 4978-4989.
Li, G. and Kim, J., Palmprint recognition with local micro-structure tetra pattern, Pattern Recognit., 2017, 61, pp. 29–46.
Diaz M.R., Travieso C.M., Alonso J. B., et al., Biometric system based in the feature of hand palm, Proc. Int. Carnahan Conf. on Security Technology, 2004, pp. 136–139.
Ajmera P.K., Jadhav D.V., and Holambe R.S., Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram, Pattern recognition, 2011, 44, (10-11) pp. 2749–2759.
Jia W., Huang D.-S., and Zhang D., Palmprint verification based on robust line orientation code, Pattern Recognit., 2008, 41, (5), pp. 1504–1513.
Lu G., Zhang D., and Wang K., Palmprint recognition using eigenpalms features, Pattern Recognit. Lett. , 2003, 24, (9), pp. 1463–1467.
Sang H., Yuan W., and Zhang Z., Research of palmprint recognition based on 2dpca, Int. Symposium on Neural Networks, 2009, pp. 831-838.
Zhao Z., Huang D., and Jia W., Palmprint recognition with 2DPCA+PCA based on modular neural networks, Neurocomputing, 2007, 71, (1), pp. 448–454.
Connie T., Michael G.K.O., Jin A.T.B., et al., An automated palmprint recognition system, Image and Vision Computing, 2005, 23, (5), pp. 501-515.
Michael G.K.O., Connie T., Jin A.T.B., Touch-less palm print biometrics: novel design and implementation, Image Vision Comput., 2008, 26, (12), pp. 1551-1560.
Luo Y., Zhao L., Zhang B., et al., Local line directional pattern for palmprint recognition, Pattern Recognit., 2016, 50, (2), pp. 26–44.
Morles A., Ferrer M.A., and Kumar A., Towards contactless palmprint authentication, IET Comput. Vis., 2011, 5, (6), pp. 407–416.
Zhang D., Kong W.K., You J., et al., On-line palmprint identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25, (9), pp. 1041-1050.
Kong A., Zhang D., and Kamel M., Palmprint identification using feature-level fusion, Pattern Recognit., 2006, 39, (3), pp. 478–487.
Kong A.-K., and Zhang D., Competitive coding scheme for palmprint verification, Proc. 17th IEEE Int. Conf. on Pattern Recognition, 2004, 1, pp. 520–523.
Xu Y., Fei L., Wen J., et al., Discriminative and Robust Competitive Code for Palmprint recognition, IEEE Trans. Syst. Man Cybern. Syst., 2018, 48, pp. 232-241.
Lunk Fei, Yong Xu and David Zhang, Half-orientation extraction of palmprint features, Pattern Recognition Letters, 2016, 69, (1), 2016, pp. 35-41.
Zhang C., Zhong W., Zhang C., and Qin X., Double Half-Orientation Code and Nonlinear Matching Scheme for Palmprint Recognition, International Conference on Mechatronics and Intelligent Roboticsvol, 2018, pp. 36-42
Poonam Poonia, Pawan K. Ajmera and Amey Bhalerao, Palm-print recognition based on scale invariant features, IEEE 16th India Council International Conference (INDICON), 2019.
P. Radu, K. Sirlantzis, W. G. J. Howells, et al., Optimizing 2D Gabor Filters for Iris Recognition, 2013 fourth International Conference on Emerging Security Technologies, 2013, pp. 47-50.
P. V. Bankar and A. C. Pise, Face recognition by using Gabor and LBP, International Conference on Communications and Signal Processing (ICCSP), 2015, pp. 0045-0048,
B. Garg, A. Chaudhary, K. Mendiratta and V. Kumar, Fingerprint recognition using Gabor Filter, International Conference on Computing for Sustainable Global Development (INDIACom), 2014, pp. 953- 958.
Chih-Jen Lee and Sheng-De Wang, Fingerprint feature extraction using Gabor filters, Electronics Letters, 1999, 35, (4), pp. 288-290.
Lu G., Zhang D. and K. Wang, Palmprint recognition using Eigenpalms features, Pattern Recognition Letters, 2003, 24, (9–10), pp. 1473–1477.
Y. Zhang, W. Li, L. Zhang, X. Ning, et al., Adaptive Learning Gabor Filter for Finger-Vein Recognition IEEE Access, 2019, 7, pp. 159821-159830.
Mehrotra R., Namuduri K.R., and Ranganathan N., Gabor filter-based edge detection, Pattern Recogn., 1992, 25, (12), pp. 1479–1494.
Meriem Dorsaf Bounneche, Larbi Boubchir and Ahmed Bouridane et al., Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters Neurocomputing, 2016, 205, pp. 274–286.
PolyUpalmprint Database: http://www.comp.polyu.edu.hk/~biometrics/:
IIT Delhi palmprint database:[Online] Available from http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.ht.
CASIA palm print database:[Online] Available from http://biometrics.idealtest.org/.
Poonam Poonia, Pawan K. Ajmera, and Vijayendra Shende, Palmprint Recognition using Robust Template Matching, Procedia Computer Science, 2020, 67, pp. 727-736.
Shahla Saedi and Nasrollah Moghadam Charkari, Palmprint authentication based on discrete orthonormal
S-Transform, Applied Soft Computing, 2014, 21, pp. 341-351.
D. Hong, W. Liu, X. Wu et al., Robust palmprint recognition based on the fast variation Vese–Osher model, Neurocomputing, 2016, 174, pp. 999-1012.
Wang J.-G., Yau W.-Y., Suwandy A., et al., Fusion of palmprint and palm vein images for person
recognition based on ‘Laplacianpalm’ feature, IEEE Conf. on Computer Vision and Pattern Recognition, 2007, pp. 1–8.
Wang J.-G., Yau W.-Y., Suwandy A., et al., Person recognition by fusing palmprint and palm vein images based on ‘Laplacianpalm’ representation, Pattern Recogn., 2008, 41, (5), pp. 1514–1527.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Trends in Opto-Electro and Optical Communications