ENHANCING PALMPRINT RECOGNITION: A NOVEL CUSTOMIZED LOOCV-DRIVEN SIAMESE DEEP-LEARNING NETWORK

(Received: 14-Jul.-2025, Revised: 17-Oct.-2025 and 29-Oct.-2025 , Accepted: 29-Oct.-2025)

Authors Wafaa Mohammed Cherif, Javier Garrigós, Juan Zapata, Tarik Boudghene Stambouli,

Keywords #Palmprint recognition #Deep learning #Customized LOOCV #Siamese network

Abstract The advancement of deep learning in biometric systems, in which face and hand modalities have been widely implemented, leads to significant improvements in terms of speed performance and data confidentiality. Palmprint recognition is the main focus of the proposed approach, which deals with databases that are relatively smaller than other biometric datasets. A large and complex deep-learning model may overfit and lose its ability to generalize when applied to such data. This study addresses this challenge by implementing a deep learning model suitable for palmprints, which are characterized by diversity and limited data. Initially, the appropriate Region of Interest (ROI) is extracted using active segmentation, which is fitting for dealing with the difficulty of obtaining palmprints from hand images with closely spaced or connected fingers. In the second stage, a novel customized LOOCV Leave-One-Out Cross Validation (A Modified-LOOCV) technique is integrated with a Siamese deep-learning network for palmprint verification. Unlike conventional LOOCV, our modified scheme optimizes the computational cost while achieving a balanced evaluation on three different datasets. The proposed framework rivals the effectiveness of the advanced palmprint-recognition systems with a high recognition accuracy of 99.75%, improved equal error rate (EER), reduced to 0.002, and faster matching time, making it highly suitable for field application.

References

[1] S. Dargan and M. Kumar, "A Comprehensive Survey on Biometric Recognition Systems Based on Physiological and Behavioral Modalities," Expert Systems with Applications, vol. 143, p. 113114, 2020.

[2] A. Genovese, V. Piuri and F. Scotti, Touchless Palmprint Recognition Systems, Part of the Book Series: Advances in Information Security (ADIS), vol. 60, Springer, 2014.

[3] A. Serrano, I. M. de Diego, C. Conde and E. Cabello, "Recent Advances in Face Biometrics with Gabor Wavelets: A Review," Pattern Recognition Letters, vol. 31, no. 5, pp. 372–381, 2010.

[4] J. Qian, J. Yang, Y. Tai and H. Zheng, "Exploring Deep Gradient Information for Biometric Image Feature Representation," Neurocomputing, vol. 213, pp. 162–171, 2016.

[5] A. Vinay, C. A. Kumar, G. R. Shenoy, K. N. B. Murthy and S. Natarajan, "ORB-PCA Based Feature Extraction Technique for Face Recognition," Procedia Computer Science, vol. 58, pp. 614–621, 2015.

[6] R. Yadav, S. K. Singh and R. Yogi, "Biometric Network Security Enhancements through Deep Learning Techniques," Proc. of the 2023 IEEE Int. Conf. on ICT in Business Industry & Government (ICTBIG), pp. 1–6, Indore, India, 2023.

[7] S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun and D. Zhang, "Biometrics Recognition Using Deep Learning: A Survey," Artificial Intelligence Review, vol. 56, no. 8, pp. 8647–8695, 2023.

[8] H. Heidari and A. Chalechale, "Biometric Authentication Using Deep Learning Based on Multi-level Fusion of Finger-knuckle Print and Fingernail," Expert Systems with Appl., vol. 191, p. 116278, 2022.

[9] D. Zhong, X. Du and K. Zhong, "Decade Progress of Palmprint Recognition: A Brief Survey," Neurocomputing, vol. 328, pp. 16–28, 2019.

[10] S. Joshi, D. K. Verma, G. Saxena and A. Paraye, "Issues in Training Convolutional Neural Networks for Image Classification," Proc. of Advances in Computing and Data Sciences (ICACDS 2019), Part of the Book Series: Comm. in Computer and Information Science (CCIS), vol. 1046, pp. 282–293, 2019.

[11] Y. Wang, J. Li, M. Zhang and G. Xu, "Palm Vein Recognition in Few-shot Learning via Modified Siamese Network," Proc. of the 2024 4th Int. Conf. on Neural Networks, Information and Communication Engineering (NNICE), pp. 598–603, Guangzhou, China, 2024.

[12] N. Elaraby, S. Barakat and A. Rezk, "A Novel Siamese Network for Few/Zero-shot Handwritten Character Recognition," Computers, Materials & Continua, vol. 74, no. 1, pp. 1837-1854, 2023.

[13] A. Hussain, A. Ullah, A. Aslam and A. Khatoon, "A Modified Siamese Network for Facial Assimilation," WSEAS Transactions on Signal Processing, vol. 19, pp. 60–66, 2023.

[14] A. Singh, A. Kumar and S. Lukose, "Forensic Acoustic Applications of Siamese Neural Networks," Proc. of the 2024 3rd Int. Conf. for Innovation in Technology (INOCON), pp. 1–4, 2024.

[15] C. R. Kumar et al., "Face Recognition Using CNN and Siamese Network," Measurement: Sensors, vol. 27, p. 100800, 2023.

[16] E. AlShemmary and F. A. Ameen, "Siamese Network-based Palmprint Recognition," Journal of Kufa for Mathematics and Computer, vol. 10, no. 1, pp. 108–118, 2023.

[17] D. M. Aprilla et al., "Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet and EfficientNetB0," J. Media Inform, Budidarma, vol. 8, no. 2, pp. 1065–1076, 2024.

[18] F. Liao, F. Leng, Z. Yang and B. Zhang, "Multi-order Extension Codes for Palmprint Recognition," International Journal of Neural Systems, vol. 35, no. 8, p. 2550039, 2025.

[19] L. Yan, F. Wang, F. Leng and A. B. J. Teoh, "Toward Comprehensive and Effective Palmprint Reconstruction Attack," Pattern Recognition, vol. 155, p. 110655, 2024.

[20] H. Shao, S. Shi, X. Du, D. Zeng and D. Zhong, "Robust Palmprint Recognition via Multi-stage Noisy Label Selection and Correction," IEEE Trans. on Image Processing, vol. 34, pp. 4591– 4601, 2025.

[21] H. Shao, C. Liu, C. Li and D. Zhong, "Privacy-preserving Palmprint Recognition via Federated Metric Learning," IEEE Transactions on Information Forensics and Security, vol. 19, pp. 878–891, 2023.

[22] H. Shao, Y. Zou, C. Liu, Q. Guo and D. Zhong, "Learning to Generalize Unseen Dataset for Crossdataset Palmprint Recognition," IEEE Trans. on Inf. Forensics and Secu., vol. 19, pp. 3788–3799, 2024.

[23] K. Zhang et al., "Palmprint Recognition Based on Gating Mechanism and Adaptive Feature Fusion," Frontiers in Neurorobotics, vol. 17, p. 1203962, 2023.

[24] J. Q. Du et al., "Advancements in Image Recognition: A Siamese Network Approach," Information Dynamics and Applications, vol. 3, no. 2, pp. 89–103, 2024.

[25] F. Marattukalam et al., "N-shot Palm Vein Verification Using Siamese Networks," Proc. of the 2021 IEEE Int. Conf. of the Biometrics Special Interest Group, pp. 1–5, Darmstadt, Germany, 2021.

[26] V. Gurunathan et al., "Palm Vein Biometric System Using Siamese Neural Network," Proc. of the 2024 IEEE Int. Conf. on Science Technology Eng. and Manag. (ICSTEM), pp. 1–5, Coimbatore, India, 2024.

[27] D. Zhong, Y. Yang and X. Du, "Palmprint Recognition Using Siamese Network," Proc. of the 13th Chinese Conf. (CCBR 2018), pp. 48–55, DOI: 10.1007/978-3-319-97909-0_6, Urumqi, China, 2018.

[28] X. Du, D. Zhong and P. Li, "Low-shot Palmprint Recognition Based on Meta-Siamese Network," Proc. of the 2019 IEEE Int. Conf. on Multimedia and Expo (ICME), pp. 79–84, Shanghai, China, 2019.

[29] H. Shao et al., "Few-shot Learning for Palmprint Recognition via Meta-Siamese Network," IEEE Transactions on Instrumentation and Measurement, vol. 70, p. 5009812, 2021.

[30] C. Liu et al., "Siamese-hashing Network for Few-shot Palmprint Recognition," Proc. of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 3251–3258, Xiamen, China, 2019.

[31] S. Younus et al., "Palmprint Recognition Using Deep Convolutional Neural Networks," Proc. of the 2022 IEEE 2nd Int. Maghreb Meeting of the Conf. on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), pp. 539–543, Sabratha, Libya, 2022.

[32] W. M. Cherif et al., "Active Contour Segmentation and CNN for Palmprint Recognition," Proc. of the 2022 2nd Int. Conf. on New Techn. of Inf. and Comm. (NTIC), pp. 1–6, Mila, Algeria, 2022.

[33] M. Ezz et al., "Improved Siamese Palmprint Authentication Using Pre-trained VGG16-palmprint," Computer Systems Science & Engineering, vol. 46, no. 2, pp. 2299-2317, 2023.

[34] R. Chen et al., "Research on Palmprint Recognition Based on Mechanism and Data," Proc. of the Int. Conf. on Computer Vision and Deep Learning (CVDL’24), Article no. 66, pp. 1–9, DOI: 10.1145/3653804.3656264, 2024.

[35] A. A. Amini et al., "Using Dynamic Programming for Minimizing the Energy of Active Contours with Hard Constraints," Proc. of the 2nd Int. Conf. on Computer Vision, pp. 95–99, Tampa, USA, 1988.

[36] K. Ito et al., "Palm Region Extraction for Contactless Palmprint Recognition," Proc. of the 2015 IEEE Int. Conf. on Biometrics (ICB), pp. 334–340, Phuket, Thailand, 2015.

[37] T. Connie et al., "Automated Palmprint Recognition System," Image and Vision Computing, vol. 23, no. 5, pp. 501–515, 2005.

[38] V. W. Lumumba, "Comparative Analysis of Cross-validation Techniques: LOOCV, K-fold and Repeated K-fold in Machine Learning Models," American J. of Theoretical and Applied Statistics, vol. 13, no. 5, pp. 127-137, 2024.

[39] Zhang, Lin: "Contactless Palmprint Database," [Online], Available: https://cslinzhang.github.io/ContactlessPalm/, Accessed: July 11, 2025.

[40] L. Zhang et al., "Towards Contactless Palmprint Recognition: A Novel Device, New Benchmark and Collaborative Representation Approach," Pattern Recognition, vol. 69, pp. 199–212, 2017.

[41] IIT Delhi Biometric Database, IIT Delhi Touchless Palmprint Database, [Online], Available: https://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_ Palm.htm, Accessed: Sep. 1, 2024.

[42] A. Kumar, "Incorporating Cohort Information for Reliable Palmprint Authentication," Proc. of the 2008 IEEE 6th Indian Conf. on Computer Vision, Graphics & Image Processing (ICVGIP), pp. 583–590, Bhubaneswar, India, 2008.

[43] M. A. Ferrer et al., "Low-cost Multi-modal Biometric Identification Based on Hand Geometry and Texture," Proc. of the 2007 41st Annual IEEE Int. Carnahan Conf. on Security Technology, pp. 52–58, Ottawa, Canada, 2007.

[44] V. Kanhangad, A. Kumar and D. Zhang, "Contactless and Pose-invariant Biometric Identification Using Hand Surface," IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1415–1424, 2010.

[45] D. Zhang, W. K. Kong, J. You and M. Wong, "Online Palmprint Identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041–1050, 2003.

[46] A. Gumaei et al., "Effective Palmprint Recognition for Visible and Multi-spectral Images," Sensors, vol. 18, no. 5, p. 1575, 2018.

[47] Z. Yang et al., "Multi-order Texture Features for Palmprint Recognition," Artificial Intelligence Review, vol. 56, no. 2, pp. 995–1011, 2023.

[48] L. Liang, T. Chen and L. Fei, "Orientation Space Code and Multi-feature Two-phase Sparse Representation for Palmprint Recognition," Int. Journal of Machine Learning and Cybernetics, vol. 11, pp. 1453–1461, 2020.

[49] T. Chai, S. Prasad and S. Wang, "Boosting Palmprint Identification with Gender Information Using DeepNet," Future Generation Computer Systems, vol. 99, pp. 41–53, 2019.

[50] C. Liu, D. Zhong and H. Shao, "Few-shot Palmprint Recognition Based on Similarity Metric Hashing Network," Neurocomputing, vol. 456, pp. 540–549, 2021.

[51] H. Shao and D. Zhong, "Towards Open-set Touchless Palmprint Recognition via Weight-based Meta Metric Learning," Pattern Recognition, vol. 121, p. 108247, 2022.

[52] Z. Yang et al., "Comprehensive Competition Mechanism in Palmprint Recognition," IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5160–5170, 2023.

[53] Z. Yang et al., "CO3Net: Coordinate-aware Contrastive Competitive Neural Network for Palmprint Recognition," IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–14, 2023.

[54] A. Fawzy, M. Ezz, S. Nouh and G. Tharwat, "Palmprint Recognition Using Siamese Network and Transfer Learning," International Journal of Advanced and Applied Sciences, vol. 9, no. 3, pp. 90–99, 2022.