An efficient deep learning framework for contactless palmprint recognition: multiresolution principal component analysis network
DOI:
https://doi.org/10.14311/AP.2026.66.0019Keywords:
palmprint recognition, PCANet, multi-resolution analysis, principal component analysisAbstract
Contactless palmprint recognition is a widely used method of personal identification. Its performance relies primarily on the feature extraction stage, where intra-variability (pose, scale, and illumination) must be considered. This study presents a novel and challenging contactless palmprint representation called the Deep Statistical Image Features (DSIF), which combines the Discrete Wavelet Transform (DWT) with the Principal Component Analysis Network (PCANet). The methodology uses the following steps: First, the DWT of levels 1 and 2 is applied to extract different sub-band images. Next, the PCANet algorithm is applied to the palmprint image and the low-frequency sub-band images. Then, histograms are extracted and concatenated. Finally, the reduced representation is constructed using Whitened Principal Component Analysis (WPCA). The key contribution of this study is its feature extraction methodology, which uses multiresolution analysis instead of multi-patch decomposition in order to obtain pertinent information from various image resolutions. The proposed method uses the entire IIT-Delhi contactless database to construct the model, which is then tested on two other contactless palmprint databases, CASIA and Tongji. The method achieved rank-1 identification rates of 99.80 % on CASIA, 98.77 % on Right Tongji, and 99.07 % on Left Tongji, results that are impressive compared to current approaches and methods.
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A. I. Awad, A. Babu, E. Barka, K. Shuaib. AI-powered biometrics for Internet of Things security: A review and future vision. Journal of Information Security and Applications 82:103748, 2024. https://doi.org/10.1016/j.jisa.2024.103748
Y. Belhocine, A. Meraoumia, K. Abderrazak, M. Saigaa. Efficient contactless palmprint recognition system based on deep rule-based classification. Acta Informatica Pragensia 13(2):193–212, 2024. https://doi.org/10.18267/j.aip.236
X. Guo, P. Zhang, C. Wang, et al. A novel deep learning model for palmprint/palmvein recognition. IEEE Access 9:122847–122854, 2021. https://doi.org/10.1109/ACCESS.2021.3110206
A. Attia, S. Mazaa, Z. Akhtar, Y. Chahir. Deep learning-driven palmprint and finger knuckle pattern-based multimodal person recognition system. Multimedia Tools and Applications 81(8):10961–10980, 2022. https://doi.org/10.1007/s11042-022-12384-3
C. Kant, S. Chaudhary. A multimodal biometric system based on finger knuckle print, fingerprint, and palmprint traits. In M. K. Sharma, V. S. Dhaka, T. Perumal, et al. (eds.), Innovations in Computational Intelligence and Computer Vision, vol. 1189, pp. 182–192. Springer Singapore, Singapore, 2021. https://doi.org/10.1007/978-981-15-6067-5_21
F. Liu, Y. Zhao, G. Liu, L. Shen. Fingerprint pore matching using deep features. Pattern Recognition 102:107208, 2020. https://doi.org/10.1016/j.patcog.2020.107208
N. Charfi, H. Trichili, A. M. Alimi, B. Solaiman. Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation. Multimedia Tools and Applications 76(20):20457–20482, 2017. https://doi.org/10.1007/s11042-016-3987-9
Z. Yang, L. Leng, A. B. J. Teoh, et al. Cross-database attack of different coding-based palmprint templates. Knowledge-Based Systems 264:110310, 2023. https://doi.org/10.1016/j.knosys.2023.110310
Z. Cao, W. Zhao, H. Zhao, L. Pang. Composite fixed-length ordered features with index-of-max transformation for high-performing and secure palmprint template protection. Journal of Information and Intelligence 3(1):51–67, 2025. https://doi.org/10.1016/j.jiixd.2024.09.002
L. Wu, Y. Xu, Z. Cui, et al. Triple-type feature extraction for palmprint recognition. Sensors 21(14):4896, 2021. https://doi.org/10.3390/s21144896
L. Fei, Y. Xu, B. Zhang, et al. Low-rank representation integrated with principal line distance for contactless palmprint recognition. Neurocomputing 218:264–275, 2016. https://doi.org/10.1016/j.neucom.2016.08.048
L. Zhang, L. Li, A. Yang, et al. Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recognition 69:199–212, 2017. https://doi.org/10.1016/j.patcog.2017.04.016
N. Amrouni, A. Benzaoui, R. Bouaouina, et al. Contactless palmprint recognition using binarized statistical image features-based multiresolution analysis. Sensors 22(24):9814, 2022. https://doi.org/10.3390/s22249814
W. Jia, B. Zhang, J. Lu, et al. Palmprint recognition based on complete direction representation. IEEE Transactions on Image Processing 26(9):4483–4498, 2017. https://doi.org/10.1109/TIP.2017.2705424
Y.-T. Luo, L.-Y. Zhao, B. Zhang, et al. Local line directional pattern for palmprint recognition. Pattern Recognition 50:26–44, 2016. https://doi.org/10.1016/j.patcog.2015.08.025
S. Zhao, B. Zhang. Learning complete and discriminative direction pattern for robust palmprint recognition. IEEE Transactions on Image Processing 30:1001–1014, 2021. https://doi.org/10.1109/TIP.2020.3039895
S. A. Maadeed, X. Jiang, I. Rida, A. Bouridane. Palmprint identification using sparse and dense hybrid representation. Multimedia Tools and Applications 78(5):5665–5679, 2019. https://doi.org/10.1007/s11042-018-5655-8
I. Rida, S. A. Maadeed, X. Jiang, et al. An ensemble learning method based on random subspace sampling for palmprint identification. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2047–2051. 2018. https://doi.org/10.1109/ICASSP.2018.8462051
D.-S. Huang, W. Jia, D. Zhang. Palmprint verification based on principal lines. Pattern Recognition 41(4):1316–1328, 2008. https://doi.org/10.1016/j.patcog.2007.08.016
X. Wu, D. Zhang, K. Wang. Palm line extraction and matching for personal authentication. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 36(5):978–987, 2006. https://doi.org/10.1109/TSMCA.2006.871797
A. W.-K. Kong, D. Zhang. Competitive coding scheme for palmprint verification. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 1, pp. 520–523. IEEE, Cambridge, UK, 2004. https://doi.org/10.1109/ICPR.2004.1334184
Z. Guo, D. Zhang, L. Zhang, W. Zuo. Palmprint verification using binary orientation co-occurrence vector. Pattern Recognition Letters 30(13):1219–1227, 2009. https://doi.org/10.1016/j.patrec.2009.05.010
L. Fei, Y. Xu, W. Tang, D. Zhang. Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognition 49:89–101, 2016. https://doi.org/10.1016/j.patcog.2015.08.001
W. Jia, D.-S. Huang, D. Zhang. Palmprint verification based on robust line orientation code. Pattern Recognition 41(5):1504–1513, 2008. https://doi.org/10.1016/j.patcog.2007.10.011
Q. Dai, N. Bi, D. Huang, et al. M-band wavelets application to palmprint recognition based on texture features. In 2004 International Conference on Image Processing, 2004, vol. 2, pp. 893–896. IEEE, Singapore, 2004. https://doi.org/10.1109/ICIP.2004.1419443
G. Lu, D. Zhang, K. Wang. Palmprint recognition using eigenpalms features. Pattern Recognition Letters 24(9–10):1463–1467, 2003. https://doi.org/10.1016/S0167-8655(02)00386-0
L. Fei, G. Lu, W. Jia, et al. Feature extraction methods for palmprint recognition: A survey and evaluation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(2):346–363, 2019. https://doi.org/10.1109/TSMC.2018.2795609
H. Doghmane, H. Bourouba, K. Messaoudi, A. Bouridane. Palmprint recognition based on discriminant multiscale representation. Journal of Electronic Imaging 27(5):053032, 2018. https://doi.org/10.1117/1.JEI.27.5.053032
S. Bendjoudi, H. Bourouba, H. Doghmane, et al. Palmprint identification performance improvement via patch-based binarized statistical image features. Journal of Electronic Imaging 28(5):053009, 2019. https://doi.org/10.1117/1.JEI.28.5.053009
A. Ignat, I. Păvăloi. Keypoint selection algorithm for palmprint recognition with SURF. Procedia Computer Science 192:270–280, 2021. https://doi.org/10.1016/j.procs.2021.08.028
T.-H. Chan, K. Jia, S. Gao, et al. PCANet: A simple deep learning baseline for image classification? IEEE Transactions on Image Processing 24(12):5017–5032, 2015. https://doi.org/10.1109/TIP.2015.2475625
J. Prakash Veigas, S. Kumari M. Deep learning approach for touchless palmprint recognition based on Alexnet and fuzzy support vector machine. International Journal of Electrical and Computer Engineering Systems 13(7):551–559, 2022. https://doi.org/10.32985/ijeces.13.7.7
S. Trabelsi, D. Samai, F. Dornaika, et al. Efficient palmprint biometric identification systems using deep learning and feature selection methods. Neural Computing and Applications 34(14):12119–12141, 2022. https://doi.org/10.1007/s00521-022-07098-4
K. Kavukcuoglu, P. Sermanet, Y.-l. Boureau, et al. Learning convolutional feature hierarchies for visual recognition. In J. Lafferty, C. Williams, J. Shawe-Taylor, et al. (eds.), Advances in Neural Information Processing Systems, vol. 23, pp. 1–9. 2010.
D. Hu, G. Feng, Z. Zhou. Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recognition 40(1):339–342, 2007. https://doi.org/10.1016/j.patcog.2006.06.022
A. S. Tarawneh, D. Chetverikov, A. B. Hassanat. Pilot comparative study of different deep features for palmprint identification in low-quality images, 2018. https://doi.org/10.48550/arxiv.1804.04602
W. Gong, X. Zhang, B. Deng, X. Xu. Palmprint recognition based on convolutional neural network – Alexnet. In Annals of Computer Science and Information Systems, pp. 313–316. 2019. https://doi.org/10.15439/2019F248
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Copyright (c) 2026 Hakim Doghmane, Zoheir Mentouri, Mohamed Cherif Amara Korba, Hocine Bourouba, Larbi Boubchir
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