Siamese Network-Based Palm Print Recognition




Convolution Neural Network (CNN), Palm Print Recognition, Siamese Neural Net (SNN))


palm print recognition is a biometric technology used to identify individuals based on their unique comfort patterns. Identifying patterns in computer vision is a challenging and interesting problem. It is an effective and reliable method for authentication and access control. In recent years, deep learning approaches have been used for handprint recognition with very good results. We suggest in this paper, a Siamese network-based approach for handprint recognition. The proposed approach consists of two convolutional neural networks (CNNs) that share weights and are trained to extract features from images of handprints, which are then compared using a loss of variance function to determine whether the two images belong to the same person or not. Among 13,982 input images, 20% are used for testing, 80% for training, and then passing each image over one of two matching subnets (CNN) that transmit weights and parameters. So that, the extracted features become clearer and more prominent. This approach has been tested and implemented using the CASIA PalmprintV1 5502 palm print database, the CASIA Multi-Spectral PalmprintV1 7,200 palm print, and the THUPALMLAB database of 1,280 palm print using MATLAB 2022a. For 13,982 palmprint recognitions in the database, the equal error rate was 0.044, and the accuracy was 95.6% (CASIA palmprintV1, THUPALMLAB, and CASIA Multi-Spectral palmprintV1). The performance of the real-time detecting system is stable and fast enough.


Download data is not yet available.


Zhang, D. (2004). Palmprint Authentication, Kluwer Academic Publishers.

Fatima A. Ameen, Ebtesam N. AlShemmary, “Palmprint Recognition Using VGG16”, International Journal on Technical and Physical Problems of Engineering, IJTPEJournal, Issue 53, Vol. 14, No. 4, p.p. 65-74, December 2022.

S. Zhao, B. Zhang, “Deep Discriminative Representation for Generic Palmprint Recognition”, Pattern Recognition, Issue 98, pp. 107071-107081, 2020.

T. Vijayakumar, “Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature”, Journal of Innovative Image Processing (JIIP), Issue 3, No. 02, pp. 131-143, 2021. DOI:

L. Fei, et al., “Double-Orientation Code and Nonlinear Matching Scheme for Palmprint Recognition”, Pattern Recognition, Vol. 49, pp. 89-101, 2016. DOI:

Z. Sun, et al., “Ordinal Palmprint Representation for Personal Identification”, The IEEE International Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 279-284, Orlando, USA, 2005.

Y. Hao, et al., “Multi-Spectral Palm Image Fusion for Accurate Contact-Free Palmprint Recognition”, The IEEE International Conference on Image Processing, pp. 281-284, USA, 2008.

J. Dai, J. Feng, J. Zhou, “Robust and Efficient Ridge-Based Palmprint Matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 8, pp.1618-1632, 2012. DOI:

Thapar, D., Jaiswal, G., Nigam, A., & Kanhangad, V. (2019, January). PVSNet: Palm vein authentication Siamese network trained using triplet loss and adaptive hard mining by learning enforced domain-specific features. In 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA) (pp. 1-8). IEEE. DOI:

Dai, J., & Zhou, J. (2010). Multifeature-based high-resolution palmprint recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 945-957. DOI:

Zhang, D., Lu, G., Li, W., Zhang, L., & Luo, N. (2009). Palmprint recognition using 3-D information. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 39(5), 505-519. DOI:

Zhao, S., & Zhang, B. (2020). Deep discriminative representation for generic palmprint recognition. Pattern Recognition, 98, 107071 DOI:

Zhang, W., Wang, Y., & Li, Z. (2019). A multi-task learning approach for palmprint recognition. Information Sciences, 506, 1-12. DOI:

A. A. et al. (2021).Attention-based multi-task network for palmprint recognition. Journal of Ambient Intelligence and Humanized Computing, 12(2), 4463-4474.

X. Y. et al. (2021). Multi-resolution attention network for palmprint recognition. Journal of Ambient Intelligence and Humanized Computing, 12(2), 4463-4474.




How to Cite

AlShemmary, E., & Ameen, F. A. (2023). Siamese Network-Based Palm Print Recognition. Journal of Kufa for Mathematics and Computer, 10(1), 108–118.

Similar Articles

1 2 3 4 > >> 

You may also start an advanced similarity search for this article.