Improving the Performance of Finger-Vein Recognition System Using A New Scheme of Modified Preprocessing Methods
This paper aims at improving the performance of finger-vein recognition system using a new scheme of image preprocessing. The new scheme includes three major steps, RGB to Gray conversion, ROI extraction and alignment and ROI enhancement. ROI extraction and alignment includes four major steps. First, finger-vein boundaries are detected using two edge detection masks each of size (4 x 6). Second, the correction for finger rotation is done by calculating the finger base line from the midpoints between the upper and lower boundaries using least square method. Third, ROI is extracted by cropping a rectangle around the center of the finger-vein which is determined using the first and second invariant moments. Forth, the extracted ROI is normalized to a unified size of 192 x 64 in order to compensate for scale changes. ROI enhancement is done by applying the technique of Contrast-Limited Adaptive Histogram Equalization (CLAHE), followed by median and modified Gaussian high pass filters. The application of the given preprocessing scheme to a finger-vein recognition system revealed its efficiency when used with different methods of feature extractors and with different types of finger-vein database. For the University of Twente Finger Vascular Pattern (UTFVP) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Line Binary Pattern (LLBP) are (99.79, 99.86 and 99.86) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.139, 0.069 and 0.035). For the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods LBP, LDP and LLBP are (99.57, 99.73 and 99.65) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.419, 0.262 and 0.341). These results outrage those of the previous state-of-art methods.
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