Improvement on Gabor Texture Feature Based Biometric Analysis Using Image Blurring

Images without blurring are usually considered as high quality samples in biometric recognition. Image acquisition systems are carefully designed in order to capture clear images. However, experimental results show that the performance of Gabor texture feature based biometric recognition methods can be improved by image blurring. The experiments were conducted on the PolyU Palmprint Database using CompCode as well as on the CASIA Iris Database using IrisCode. The blurring method is to adopt a Gaussian filter to the images during pre-processing. Results indicate that there is an optimal range of each dataset and if all the images are blurred to this range, the performance of the whole dataset will reach optimal. A scheme is also proposed to find the optimal range and to blur an image to this range.

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