Turkish Journal of Electrical Engineering and Computer Sciences
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.
DOI
10.3906/elk-1309-65
Keywords
Palmprint, discrete cosine transform, autoregressive signals modeling
First Page
1768
Last Page
1781
Recommended Citation
ERGEN, BURHAN
(2016)
"Scale invariant and fixed-length feature extraction by integrating discrete cosine transform and autoregressive signal modeling for palmprint identification,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 24:
No.
3, Article 75.
https://doi.org/10.3906/elk-1309-65
Available at:
https://journals.tubitak.gov.tr/elektrik/vol24/iss3/75
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons