Abstract
Dorsal hand vein identification has been recently given greater attention in human recognition and it’s becoming increasingly an active topic in research. This paper presents a personal identification method based on dorsal hand vein texture. The Method includes four steps, in the first one, pre-processing phase is applied on the image contrast in order to produce a better quality of dorsal hand vein image, then region of interest (ROI) is extracted, in the second step, we have proposed a novel encoding method based on Nonsubsampled contourlet transform (NSCT) and phase response information then we divided the resulting image into local region, and statistical descriptors are calculated in each block in order to reduce the size of the characteristic vector and create a code of 512 bytes. Then, we computed the modified Hamming distance between templates to find out the similarity between two dorsal hand veins.
The method is tested on the “GPDSvenasCCD” database. The experimental results illustrate the effectiveness of this coding in Identification mode of biometric dorsal hand vein: 99.96% of rank-one recognition rate. Therefore, the coding process is presented to achieve more satisfactory results than performed by traditional statistical based approaches. The performed numerical results prove the robustness of our approach to extract discriminative features of dorsal hand veins texture, which suggests a significant advance in texture Identification.
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Oueslati, A., Feddaoui, N., Hamrouni, K. (2018). A Human Identification Technique Through Dorsal Hand Vein Texture Analysis Based on NSCT Decomposition. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_18
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DOI: https://doi.org/10.1007/978-3-319-76357-6_18
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