Novel Image Quality Assessment and Enhancement Techniques for Finger Vein Recognition As a secure and reliable biometric trait, finger vein recognition (FVR) can be employed to verify the individuals in realtime applications. However, the pattern of vein is unclear in some finger vein images due to light scattering by the skin and nonuniform illumination, which deteriorates the performance of the FVR system. To deal with the image quality problem, a novel fingervein image quality assessment method and an enhancement method are proposed. The proposed FVR Scheme is based on two folds: (i) Image Quality Assessment, and (ii) Image Enhancement. First, the quality of the image is assessed by the decision tree with rsmote technique, to classify the finger vein image into two classes, i.e. High Quality (HQ) and Low Quality (LQ) images. Second, a single scale retinex filter (SSR) with chromaticity preserved algorithm and Gaussian filter are proposed to enhance the low and high quality finger vein images. Total of 1052 finger vein images are employed for the testing aspect of quality evaluation, enhancement and recognition method. After that, low error rate EER of 0.0379 is obtained by the proposed art. Finally, the achieved results show the strength of the proposed art is better than already developed methods in FVR domain.

Multisample Compression of Finger Vein Images using H.265 Video Coding A new videocompression based approach extending traditional biometric sample data compression techniques is evaluated in the context of finger vein recognition. The proposed scheme is implemented in HEVC  H.265 in different settings and compared to (i) compressing each sample individually with JPEG2000 according to ISOIEC 197949:2011 and to (ii) compressing each usersxe2x80x99 data into an individual video file. Compression efficiency and implications on recognition accuracy are determined using 4 recognition schemes and 2 data sets, both based on publicly available data. Results obtained using the proposed approach are fairly stable across different recognition schemes and data sets and indicate a significant improvement over the current state of the art.