Scale invariant and fixed-length feature extraction by integrating discrete cosine transform and autoregressive signal modeling for palmprint identification

Authors : Burhan Ergen

Pages : 1768-1781

View : 10 | Download : 6

Publication Date : 9999-12-31

Article Type : Makaleler

Abstract :Recently, the need for automatic identification has caused researchers to focus on biometric identification methods. Palmprint-based biometric identification has several advantages such as user friendliness, low-cost capturing devices, and robustness. In this paper, a method that integrates the discrete cosine transform (DCT) and an autoregressive (AR) signal modeling is proposed for biometric identification. The method provides scale invariance and produces a fixed-length feature vector. In particular, the Burg algorithm is used for the determination of the AR parameters used as a feature vector. Experimental results demonstrate that a small number of the AR parameters that are modeling the DCT coefficients of a palmprint are sufficient to constitute a practically applicable identification system achieving a correct recognition rate of 99.79%. The accuracy of the proposed approach is not overly dependent on the number of training samples, another advantage of the method.
Keywords : Palmprint, discrete cosine transform, autoregressive signals modeling