Gammadion based Local Binary Pattern with Shearlet coefficients for Palmprint Recognition
Keywords:
Gammadion Binary Patterns, Shearlet transform, Palmprint recognition, Illumination invariantAbstract
Palmprints are stable and unique information used for biometric identification. This paper devises a novel texture-based feature extraction method inspired by the Gammadion structure that recognizes an individual based on Palmprint. The Region of Interest (ROI) extracted from the palm is initially normalized using the Histogram equalization technique. The ROI is converted into the frequency domain using Shearlet transformation to represent it in an illumination invariant form. Then the gammadion structure-based feature extraction method is used. It generates multiple feature maps fed to a simple Convolutional Neural Network(CNN). The proposed methodology is invariant with illumination and noise. Four publicly accessible standard Palmprint databases (CASIA, IITD, Tongji, and PolyU2) are used for extensive experimentation. The result is then compared with existing state-of-art techniques. The experimental analysis shows that the proposed methodology obtains the highest accuracy of 99.45% for the PolyU2 dataset, which is superior to some existing methods in the literature.
Downloads
References
N. Duta, A. K. Jain, and K. V. Mardia, “Matching of palmprints,” Pattern Recognit. Lett., vol. 23, no. 4, pp. 477–485, Feb. 2002.
A. Morales, M. A. Ferrer, and A. Kumar, “Towards contactless palmprint authentication,” IET Comput. Vis., vol. 5, no. 6, pp. 407–416, 2011.
D. Zhang, W. K. Kong, J. You, and M. Wong, “Online palmprint identification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1050, 2003.
and A. B. Abdallah Meraoumia, Farid Kadri, Hakim Bendjenna, Salim Chitroub, “Improving Biometric Identification Performance Using PCANet Deep Learning and Multispectral Palmprint,” Biometric Secur. Priv., pp. 51–69, 2017.
X. Wang, L. Lei, and M. Wang, “Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network,” Knowledge-Based Syst., vol. 27, pp. 451–455, 2012.
J. Dai, J. Zhou, and S. Member, “Multifeature-Based High-Resolution Palmprint Recognition,” vol. 33, no. 5, pp. 945–957, 2011.
J. Grover and M. Hanmandlu, “The fusion of multispectral palmprints using the information set based features and classifier,” Eng. Appl. Artif. Intell., vol. 67, pp. 111–125, Jan. 2018.
S. Trabelsi, D. Samai, F. Dornaika, A. Benlamoudi, K. Bensid, and A. Taleb-Ahmed, “Efficient palmprint biometric identification systems using deep learning and feature selection methods,” Neural Comput. Appl., vol. 4, 2022.
L. Fei, G. Lu, W. Jia, S. Teng, and D. Zhang, “Feature extraction methods for palmprint recognition: A survey and evaluation,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 2, pp. 346–363, Feb. 2019.
A. S. Tarawneh, D. Chetverikov, and A. B. Hassanat, “Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images,” Ninth Hungarian Conf. Comput. Graph. Geom., pp. 3–8, 2018.
D. Zhong, X. Du, and K. Zhong, “Decade progress of palmprint recognition: A brief survey,” Neurocomputing, vol. 328, pp. 16–28, 2019.
A. El Idrissi, Y. El Merabet, and Y. Ruichek, “Palmprint recognition using state-of-the-art local texture descriptors: A comparative study,” IET Biometrics, vol. 9, no. 4, pp. 143–153, 2020.
T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002.
X. Wang, H. Gong, H. Zhang, B. Li, and Z. Zhuang, “Palmprint identification using boosting local binary pattern,” Proc. - Int. Conf. Pattern Recognit., vol. 3, pp. 503–506, 2006.
X. Y. Jing and D. Zhang, “A face and palmprint recognition approach based on discriminant DCT feature extraction,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 6, pp. 2405–2415, 2004.
L. Zhang and D. Zhang, “Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling,” vol. 34, no. 3, pp. 1335–1347, 2004.
F. Liu, L. Zhou, Z. M. Lu, and T. Nie, “Palmprint feature extraction based on curvelet transform,” J. Inf. Hiding Multimed. Signal Process., vol. 6, no. 1, pp. 131–139, 2015.
Y. Wang and Q. Ruan, “Dual-tree complex wavelet transform based local binary pattern weighted histogram method for palmprint recognition,” Comput. Informatics, vol. 28, no. 3, pp. 299–318, 2009.
G. Kutyniok, W.-Q. Lim, and X. Zhuang, “Digital Shearlet Transform,” pp. 1–39, 2011.
H. Suo et al., “Polarization property of terahertz wave emission from gammadion-type photoconductive antennas,” Appl. Phys. Lett., vol. 103, no. 11, 2013.
S. Koley, H. Roy, and D. Bhattacharjee, “Gammadion binary pattern of Shearlet coefficients (GBPSC): An illumination-invariant heterogeneous face descriptor,” Pattern Recognit. Lett., vol. 145, pp. 30–36, 2021.
D. S. Huang, W. Jia, and D. Zhang, “Palmprint verification based on principal lines,” Pattern Recognit., vol. 41, no. 4, pp. 1316–1328, 2008.
X. Wu, D. Zhang, K. Wang, and B. Huang, “Palmprint classification using principal lines,” Pattern Recognit., vol. 37, no. 10, pp. 1987–1998, Oct. 2004.
Y. Xu, L. Fei, and D. Zhang, “Combining left and right palmprint images for more accurate personal identification,” IEEE Trans. Image Process., vol. 24, no. 2, pp. 549–559, 2015.
S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Trans. Image Process., vol. 18, no. 5, pp. 1107–1118, 2009.
Y. T. Luo et al., “Local line directional pattern for palmprint recognition,” Pattern Recognit., vol. 50, pp. 26–44, 2016.
S. Ma, Q. Hu, S. Zhao, W. Wu, and J. Wu, “Multi-Scale Multi-Direction Binary Pattern Learning for Discriminant Palmprint Identification,” IEEE Trans. Instrum. Meas., vol. 72, 2023.
L. Fei, B. Zhang, Y. Xu, D. Huang, W. Jia, and J. Wen, “Local Discriminant Direction Binary Pattern for Palmprint Representation and Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. PP, no. c, p. 1, 2019.
Tee, Connie and Ngo, David Chek Ling and Goh, Michael Kah Ong and Teoh, Andrew Beng Jin (2003) Palmprint Recognition with PCA and ICA. Conference of Image and Vision Computing New Zealand 2003 (IVCNZ’03). pp. 227-232.
S. Zhao and B. Zhang, “Learning Salient and Discriminative Descriptor for Palmprint Feature Extraction and Identification,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 12, pp. 5219–5230, 2020.
Z. Sun, T. Tan, Y. Wang, and S. Z. Li, “Ordinal palmprint represention for personal identification,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. I.
Z. Sun, L. Wang, and T. Tan, “Ordinal Feature Selection for Iris and Palmprint Recognition,” 3922 IEEE Trans. IMAGE Process. VOL. 23, NO. 9, Sept. 2014, vol. 23, no. 9, pp. 3922–3934, 2014.
L. Fei, Y. Xu, W. Tang, and D. Zhang, “Double-orientation code and nonlinear matching scheme for palmprint recognition,” Pattern Recognit., vol. 49, pp. 89–101, 2016.
K. Krajnak, S. Waugh, R. Miller, S. Li, and M. L. Kashon, “PCANet: A Simple Deep Learning Baseline for Image Classification?,” 40th Int. Congr. Expo. Noise Control Eng. 2011, INTER-NOISE 2011, vol. 4, no. 12, pp. 3552–3556, 2011.
A. Genovese, V. Piuri, K. N. Plataniotis, and F. Scotti, “PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 12, pp. 3160–3174, Dec. 2019.
J. P. Veigas, M. S. Kumari, and G. S. Satapathi, “Genetic Algorithm Based Gabor CNN For Palmprint Recognition,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 4895–4899, 2020.
C. Naveena, S. Rangappa, and Chethan H. K., “Texture Features in Palmprint Recognition System,” Int. J. Nat. Comput. Res., vol. 10, no. 1, pp. 41–57, Jan. 2021.
L. Fei, B. Zhang, S. Teng, Z. Guo, S. Li, and W. Jia, “Joint Multiview Feature Learning for Hand-Print Recognition,” IEEE Trans. Instrum. Meas., vol. 69, no. 12, pp. 9743–9755, Dec. 2020.
A. Kumar, “Toward More Accurate Matching of Contactless Palmprint Images under Less Constrained Environments,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 1, pp. 34–47, 2019.
S. Zhao and B. Zhang, “Deep discriminative representation for generic palmprint recognition,” Pattern Recognit., vol. 98, Feb. 2020.
T. Shu, B. Zhang, and Y. Y. Tang, “Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier,” IEEE Access, vol. 7, pp. 49669–49679, 2019.
S. Zhao, J. Wu, B. Zhang, and L. Fei, “Low-rank inter-class sparsity based semi-flexible target least squares regression for feature representation,” Pattern Recognit., vol. 123, Mar. 2022.
S. C. Huang, F. C. Cheng, and Y. S. Chiu, “Efficient contrast enhancement using adaptive gamma correction with weighting distribution,” IEEE Trans. Image Process., vol. 22, no. 3, pp. 1032–1041, 2013.
Y. F. Yu, D. Q. Dai, C. X. Ren, and K. K. Huang, “Discriminative multi-layer illumination-robust feature extraction for face recognition,” Pattern Recognit., vol. 67, no. 6, pp. 201–212, 2017.
L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, “Extended local binary patterns for texture classification,” Image Vis. Comput., vol. 30, no. 2, pp. 86–99, Feb. 2012.
H.Li, B.S. Manjunath, and S.K. Mitra, “Multisensor Image Fusion Using the Wavelet Transform,” Graphical Models and Image Processing, vol. 57, no. 3. pp. 235–245, 1995.
Tamilselvi, T. ., Lakshmi, D. ., Lavanya, R. ., & Revathi, K. . (2023). Digital Companion for Elders in Tracking Health and Intelligent Recommendation Support using Deep Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 145–152. https://doi.org/10.17762/ijritcc.v11i3.6331
Maria Gonzalez, Machine Learning for Anomaly Detection in Network Security , Machine Learning Applications Conference Proceedings, Vol 1 2021.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
