Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition

Abstract

As a novelist and the most secure biometric method, finger vein recognition has gained substantial significance and various pertinent researches have been reported in literature. However, it is difficult to extract a more reliable and accurate finger vein pattern due to the random noise, poor lighting, illumination variation, image deformation and blur. Furthermore, improper parameter settings of SVMs lead to poor classification accuracy and apparently, not much relevant research has been conducted on its optimal parameter setting. To alleviate these problems, this paper proposes an efficient finger vein recognition framework consisting of the hybrid Local Phase Quantization (LPQ) for robust feature extraction and Grey Wolf Optimization based SVM (GWO-SVM) to compute the best parameter combination of SVM for optimal results of binary classification. Finger vein features are first extracted by integrating LPQ, which is invariant to motion blur and deformation, with Local Directional Pattern (LDP), which is robust to random noise and illumination variation, to augment the recognition performance and reduce the computational time. Then, GWO-SVM is used for classification in order to maximize the classification accuracy by determining the optimal SVM parameters. The extensive experimental results indicate remarkable performance and significant enhancements in terms of recognition accuracy by the proposed framework compared to the existing techniques and prove the effectiveness of the proposed framework on four tested finger vein datasets. It has outperformed the typical SVM approach and kNCN-SRC two-stage methodology via achieving the recognition accuracy of 98% and equal error rate as low as 0.1020%.