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.

Comparison of compression algorithms impact on fingerprint and face recognition accuracy The impact of using different lossy compression algorithms on the matching accuracy of fingerprint and face recognition systems is investigated. In particular, we relate ratedistortion performance as measured in PSNR to the matching scores as obtained by the recognition systems. JPEG2000 and SPIHT are correctly predicted by PSNR to be the most suited compression algorithms to be used in fingerprint and face recognition systems. Fractal compression is identified to be least suited for the use in the investigated recognition systems, although PSNR suggests JPEG to deliver worse recognition results in the case of face imagery. JPEG compression performs surprisingly well at high bitrates in face recognition systems, given the low PSNR performance observed.