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

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.