A NEW FINGER-VEIN RECOGNITION SYSTEM USING THE COMPLETE LOCAL BINARY PATTERN AND THE PHASE ONLY CORRELATION

• Main Authors: Ahmed A. Mustafa, Ahmed AK. Tahir
• Format: Article
• Language: English
• Published: XLESCIENCE 2021-06-01
• Series: International Journal of Advances in Signal and Image Sciences
• Subjects: biometrics, finger vein, feature extraction, phase only correlation
• Online Access: https://xlescience.org/index.php/IJASIS/article/view/70

A new system for finger-vein recognition is proposed based on the Complete Local Binary pattern (CLBP) as afeature extractor and the Phase Only Correlation (POC) for post-processing alignment and for speeding up the system. The CLBP produces three components of image descriptors and thus holds more details compared to the previous methods such as the Local Binary Pattern (LBP), the Local Directional Pattern (LDP), the Local Line Binary Pattern (LLBP), the Repeated Line Tracking (RLT), the Maximum Curvature (MC) and the Wide Line Detector (WLD). In the proposed system, POC is used for two purposes. First, to increase the performance of the system the alignment between the CLBP components of the test image and the enrolled CLBP components are performed. Second, to speed up the matching stage, a portion of the enrolled images is excluded that are highly misaligned with the test image from the Hamming Distance (HD) measure competition in the matching stage. To make the system more secure against attacks targeting personal information, only CLBP components are enrolled in the system and the alignment process POC is implemented on these components without the need to original images. For image pre-processing a novel scheme of pre-processing methods is adopted including finger-vein localization, alignment, and the Region-Of-Interest (ROI) extraction and enhancement. Two databases, UTFVP and SDUMLA-HMT, are used to evaluate the performance of the system. The results have shown that the values for the Identification Recognition Rate (IRR) and the Equal Error Rate (EER) are respectively (99.66%) and (0.139) for the UTFVP database and (98.95%, and 0.53%) for SDUMLA-HMT database. These results are competitive compared to those achieved by the state-of-art systems.