FKP based personal authentication using SIFT features extracted from PIP joint

Hand biometric systems have been adopted in a variety of access control application because hands are rich with unique line, point, vein, and texture patterns which even low resolution scanners can easily capture. In this article, finger dorsal surface is utilized to extract the local texture information by SIFT and further modified by LDA algorithm. Finally, the test and training images are compared in terms of employing a KNN classifier. The performance of proposed algorithm is tested over a standard benchmark FKP databases (PolyU) with different number of training samples. The obtained results clarify the effectiveness of proposed method with the other state-of-the-art algorithms in terms of the Correct Recognition Rate, Equal error rate, and Computation Time.