Abstract
Large-scale data-driven DNN models have been proven to achieve great performance in various computer vision challenges, and transfer learning is proposed recently to take advantage of pre-trained DNN on a small database. Under such a framework, an innovative classification model for both identity and gender classification with hand vein information is proposed in this paper. By adopting pre-trained VGG and AlexNet model with ImageNet database and the corresponding fine-tuned ones with PolyU fingerprint and palmprint database, state-of-the-art classification results are obtained with the fine-tuned ones, which indicates that domain-specific model performs better than a generic one, and similar experimental results with faces further indicate that biometric traits share latent patterns. On the other hand, to evaluate the distribution of shared patterns, a quantized shared-index calculated as the number of correlated dictionary atoms is realized based on a sparse representation model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, G., Sun, C., Sowmya, A.: Multi-weighted co-occurrence descriptor encoding for vein recognition. IEEE Trans. Inf. Forensics Secur. 15, 375–390 (2020)
Huang, D., Tang, Y., Wang, Y., et al.: Hand-dorsa vein recognition by matching local features of multisource keypoints. EEE Trans. Cybern. 45(9), 1823–1837 (2014)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (2015)
Gary, B.H., Erik, L.M.: Labeled faces in the wild: updates and new reporting procedures. Technical report UM-CS-2014-003, University of Massachusetts, Amherst, May (2014)
Karen, S., Andrew, Z.: Very deep convolutional networks for large-scale image recognition. ArXiv e-prints, September (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, Lake Tahoe, Nevada, December (2012)
Bioucas-Dias, J.M., Figueiredo, M.A.T.: A new twist: two-step iterative shrinkage/thresholding algorithm for image restoration. IEEE Trans. Image Process. 54(11), 4311–4322 (2007)
Wang, G., Sun, C., Sowmya, A.: Learning a compact vein discrimination model with GANerated samples. IEEE Trans. Inf. Forensics Secur. 15, 635–650 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shen, Z., Wang, J., Wang, G., Pan, Z. (2021). Biometric Traits Share Patterns. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-2336-3_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2335-6
Online ISBN: 978-981-16-2336-3
eBook Packages: Computer ScienceComputer Science (R0)