Abstract
Palm-print recognition system is extensively deployed in a variety of applications, ranging from forensic to mobile phones. This paper proposes a new feature extraction technique for robust palm-print based recognition. The method combines the angle information of an edge operator and multi-scale uniform patterns, which extracts texture patterns at different angular space and spatial resolution. Thus, making the extracted uniform patterns less sensitive to the pixel level values. Further, an optimal artificial neural network structure is developed for classification, which helps in maintaining the higher classification accuracy by significantly reducing the computational complexity. The proposed method is tested on standard PolyU, IIT-Delhi and CASIA palm-print databases. The method yields an equal error rate of 0.2% and classification accuracy of 98.52% on PolyU database.
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The datasets generated during and/or analyzed during the current study are not publicly available due to the further research work but are available from the corresponding author on reasonable request.
References
Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20.
Valdes-Ramirez, D., Medina-Pérez, M. A., & Monroy, R. (2021). An ensemble of fingerprint matching algorithms based on cylinder codes and mtriplets for latent fingerprint identification. Pattern Analysis and Applications, 24, 433–444. https://doi.org/10.1007/s10044-020-00911-7
Nachar, R., Inaty, E., Bonnin, P. J., et al. (2020). Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance. Pattern Analysis and Applications, 23, 213–224. https://doi.org/10.1007/s10044-018-00766-z
Langoni, V., & Gonzaga, A. (2020). Evaluating dynamic texture descriptors to recognize human iris in video image sequence. Pattern Analysis and Applications, 23, 771–784. https://doi.org/10.1007/s10044-019-00836-w
Lima, V. C. D., Melo, V. H. C., & Schwartz, W. R. (2021). Correction to: Simple and efficient pose-based gait recognition method for challenging environments. Pattern Analysis and Applications, 24, 509. https://doi.org/10.1007/s10044-020-00945-x
Lima, V. C. D., Melo, V. H. C., & Schwartz, W. R. (2021). Simple and efficient pose-based gait recognition method for challenging environments. Pattern Analysis and Applications, 24, 497–507. https://doi.org/10.1007/s10044-020-00935-z
Aguado-Martínez, M., Hernández-Palancar, J., Castillo-Rosado, K., et al. (2021). Document scanners for minutiae-based palmprint recognition: A feasibility study. Pattern Analysis and Applications, 24, 459–472. https://doi.org/10.1007/s10044-020-00923-3
Kamboj, A., Rani, R., Nigam, A., et al. (2021). CED-Net: Context-aware ear detection network for unconstrained images. Pattern Analysis and Applications, 24, 779–800. https://doi.org/10.1007/s10044-020-00914-4
Ajmera, P., Jadhav, R., & Holambe, R. S. (2011). Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram. Pattern Recognition, 44(10–11), 2749–2759.
Kagawade, V. C., & Angadi, S. A. (2021). Savitzky-Golay filter energy features-based approach to face recognition using symbolic modeling. Pattern Analysis and Applications. https://doi.org/10.1007/s10044-021-00991-z
Ayeche, F., & Alti, A. (2021). HDG and HDGG: An extensible feature extraction descriptor for effective face and facial expressions recognition. Pattern Analysis and Applications. https://doi.org/10.1007/s10044-021-00972-2
Nakouri, H. (2021). Two-dimensional Subclass Discriminant Analysis for face recognition. Pattern Analysis and Applications, 24, 109–117. https://doi.org/10.1007/s10044-020-00905-5
Qingqiao, H., Siyang, Y., Huiyang, N., et al. (2020). An end to end deep neural network for iris recognition. Procedia Computer Science, 174, 505–517.
Zhang, S., & Gu, X. (2013). Palmprint recognition based on the representation in the feature space. Optik, 124, 5434–5439.
Jing, L., Jian, C., & Kaixuan, L. (2013). Improve the two-phase test samples representation method for palmprint recognition. Optik, 1124(24), 6651–6656.
Ali, M. M., Yannawar, P., & Gaikwad, A. T. (2016, March). Study of edge detection methods based on palmprint lines. In International Conference on Electrical, Electronics, and Optimization Techniques (pp. 1344-1350).
You, J., Li, W. X., & Zhang, D. (2002). Hierarchical palmprint identification via multiple feature extraction. Pattern Recognition, 35, 847–859.
Zhang, D., Kong, W. K., You, J., et al. (2003). On-line palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence., 25(9), 1041–1050.
Diaz, M. R., Travieso, C. M. Alonso, J. B.. et al. (2004). Biometric system based in the feature of hand palm. In Proceedings of International Carnahan Conference on Security Technology (pp. 136–139).
Kong, A., Zhang, D., & Kamel, M. (2006). Palmprint identification using feature-level fusion. Pattern Recognit., 39(3), 478–487.
Sun, Z., Tan, T., Wang, Y., et al. (2005). Ordinal palmprint representation for personal identification. 2005. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (pp. 279–284).
Jia, W., Hu, R. X., Lei, Y. K., et al. (2014). Histogram of oriented lines for palmprint recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(3), 385–395.
Mokni, R., Hassen, D., & Monji, K. (2017). Combining shape analysis and texture pattern for palmprint identification. Multimedia Tools and Applications, 76, 23981–24008.
Luo, Y. T., Zhao, L. Y., Bob, Z., et al. (2016). Local line directional pattern for palmprint recognition. Pattern Recognition, 50, 26–44.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59.
Tamrakar, D., & Khanna, P. (2015). Occlusion invariant palmprint recognition with ULBP histograms. In Int. conf. on Image and Signal processing (pp. 491–500).
Guo, X., Zhou, W., & Yanli, Z. (2017). Collaborative representation with HM-LBP features for palmprint recognition. Machine Vision and Applications., 28, 283–291.
Li, G., & Kim, J. (2017). Palmprint recognition with local micro-structure tetra pattern. Pattern Recognition, 61, 29–46.
Zhang, S., Wang, H., Wenzhun, H., et al. (2018). Combining Modified LBP and Weighted SRC for Palmprint Recognition. Signal, Image and Video Processing, 12, 1035–1042.
Karanwal, S., & Diwakar, M. (2021). Neighborhood and center difference-based-LBP for face recognition. Pattern Analysis and Applications, 24, 741–761. https://doi.org/10.1007/s10044-020-00948-8
Michael, G. K. O., Connie, T., & Jin, A. T. B. (2008). Touch-less palm print biometrics: Novel design and implementation. Image and Vision Computing, 26(12), 1551–1560.
Shorrock, S., Yannopoulos, A., Dlay, S., et al. (2000). Biometric verification of computer users with probabilistic and cascade forward neural networks (pp. 267–272). Advances in Physics.
Connie, T., Jin, A. T. B., Ong, M. G. K., et al. (2005). An automated palmprint recognition system. Image and Vision Computing., 23(5), 501–515.
Nigam, A., & Gupta, P. (2015). Designing an accurate hand biometric based authentication system fusing finger knuckle print and palmprint. Neurocomputing, 151(1), 120–132.
Jaswal, G., Amit, K., & Ravinder, N. (2018). Multiple feature fusion for unconstrained palm print authentication. Computers and Electrical Engineering, 72, 53–78.
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Ukil, A., Shah, V. H., & Deck, B. (2011). Fast computation of arctangent functions for embedded applications: a comparative analysis. In IEEE International Sym-posium on Industrial Electronics (pp. 1206–1211).
Huang, T., Burnett, J., & Deczky, A. (1975). The importance of phase in image processing filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(6), 529–542.
Oppenheim, A. V., & Lim, J. S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69(5), 529–541.
Mazumdar, D., Mitra, S., Ghosh, K., et al. (2021). Analysing the patterns of spatial contrast discontinuities in natural images for robust edge detection. Pattern Analysis and Applications. https://doi.org/10.1007/s10044-021-00976-y
Junli, L., Gengyun, Y., & Guanghui, Z. (2012). Evaluation of tobacco mixing uniformity based on chemical composition. In 31st Chinese Control Conference (pp. 7552–7555).
Fausett, L. V., & Hall, P. (1994). Fundamentals of neural networks: Architectures, algorithms, and applications. Prentice-Hall.
Woo, W., & Dlay, S. (2005). Regularised nonlinear blind signal separation using sparsely connected network. IEE Proceedings-Vision, Image and Signal Processing, 152(1), 61–73.
Kou, J., Xiong, S., & Wan, S. (2010). The incremental probabilistic neural network. Sixth International Conference on Natural Computation., 3, 1330–1333.
Kwak, C., Ventura, J. A., & Tofang-Sazi, K. (2000). A neural network approach for defect identification and classification on leather fabric. Journal of Intelligent Manufacturing, 11, 485–499.
Li, Y., & Zhang, C. (2016). Automated vision system for fabric defect inspection using Gabor filters and PCNN. Springerplus. https://doi.org/10.1186/s40064-016-2452-6
Wei, P., Liu, C., Liu, M., Gao, Y., & Liu, H. (2018). CNN-based reference comparison method for classifying bare PCB defects. J. Eng., 2018(16), 1528–1533. https://doi.org/10.1049/joe.2018.8271
Liu, F., Su, L., Fan, M., Yin, J., He, Z., & Lu, X. (2017). Using scanning acoustic microscopy and LM-BP algorithm for defect inspection of micro solder bumps. Microelectronics Reliability, 79, 166–174. https://doi.org/10.1016/j.microrel.2017.10.029
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks, Piscataway (pp. 1942–1948).
Wang, D., Tan, D., & Liu, L. (2017). Particle swarm optimization algorithm: An overview. Soft Computing, 22, 387–408.
PolyU palmprint database. Available at http://www.comp.polyu.edu.hk/~biometrics/:
CASIA palm-print image database: Available at http://biometrics.idealtest.org/
IIT Delhi touchless palmprint database. Available athttp://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm
Al-Nima, R. R. O., Dlay, S. S., Woo, W. L., et al. (2016). A novel biometric approach to generate ROC curve from the probabilistic neural network. In 24th Signal Processing and Communication Application Conference, SIU. (pp. 141–144).
Wang, X., Gong, H., Zhang, H., Li, B., et al. (2016). Palmprint identification using boosting local binary pattern. International Conference on Pattern Recognition, 3, 503–506.
Jabid, T., Kabir, M. H., & Chae, O. (2010). Robust facial expression recognition based on local directional pattern. ETRI Journal, 32(5), 784–794.
Tarawneh, A. S., Chetverikov, D., and Hassanat, A. B. (2018). Pilot comparative study of different deep features for palmprint identification in low-quality images. https://arxiv.org/abs/1804.04602
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All authors contributed to the study conception and design. The first draft of the manuscript was written by Poonam Poonia and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Poonia, P., Ajmera, P.K. Robust Palm-print Recognition Using Multi-resolution Texture Patterns with Artificial Neural Network. Wireless Pers Commun 133, 1305–1323 (2023). https://doi.org/10.1007/s11277-023-10819-0
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DOI: https://doi.org/10.1007/s11277-023-10819-0