Extracting Discriminative Features by utilizing Optimum Arc_Gabor Filter-Bank for Authentication Using Palm-Print

proper choice for descripting images captured by ordinary optic sensors. In order to cover all spectrum and extracting better features filter banks are usually used. Although there is different scales and orientations in filter bank, but using proper values for other parameters such as maximum frequency, filters’ dimension and length of arc can effectively impact on final result. In this paper Meta-heuristic methods are used to estimate optimum values for these parameters. According to obtained results, in identification using Optimum Arc-Gabor Filter Bank (OAGFB) trained by Improved Gravitational Search Algorithm, the average of 1st Rank identification rate is increased from 79.43 to 95.71% and in verification by optimizing proposed filter bank using Simulated Annealing the average of Equal Error Rate is decreased from 8.84 to 5.12%.