A novel fingerprint recognition algorithm based on VK-LLE

Jing Luo; Shu-zhong Lin; Jian-yun Ni; Li-mei Song

doi:10.1117/12.836795

10 July 2009 A novel fingerprint recognition algorithm based on VK-LLE

Jing Luo, Shu-zhong Lin, Jian-yun Ni, Li-mei Song

Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 748910 (2009) https://doi.org/10.1117/12.836795
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

Abstract

It is a challenging problem to overcome shift and rotation and nonlinearity in fingerprint images. By analyzing the shortcoming of fingerprint recognition algorithm on shift or rotation images at present, manifold learning algorithm is introduced. A fingerprint recognition algorithm has been proposed based on locally linear embedding of variable neighbourhood k (VK-LLE). Firstly, approximate geodesic distance between any two points is computed by ISOMAP ( isometric feature mapping) and then the neighborhood is determined for each point by the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. Secondly, the dimension of fingerprint image is reduced by nonlinear dimension-reduction method. And the best projected features of original fingerprint data of large dimension are acquired. By analyzing the changes of recognition accuracy with the neighborhood and embedding dimension, the neighborhood and embedding dimension is determined at last. Finally, fingerprint recognition is accomplished by Euclidean distance Classifier. The experimental results based on standard fingerprint datasets have verified the proposed algorithm had a better robustness to those fingerprint images of shift or rotation or nonlinearity than the algorithm using LLE, thus this method has some values in practice.

Citation Download Citation

Jing Luo, Shu-zhong Lin, Jian-yun Ni, and Li-mei Song "A novel fingerprint recognition algorithm based on VK-LLE", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 748910 (10 July 2009); https://doi.org/10.1117/12.836795

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KEYWORDS

Detection and tracking algorithms

Fingerprint recognition

Pattern recognition

Data acquisition

Dimension reduction

Feature extraction

Electrical engineering

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