Multi-sample Compression of Iris Images Using High Efficiency Video Coding

Abstract

When multiple image samples of a single eye are captured during enrolment the accuracy of iris recognition systems can be substantially improved. However, storage requirement significantly increases if the system stores multiple iris images per enrolled eye. We consider this practical scenario and provide a comparative study on the usefulness of relevant image compression algorithms, i.e. JPEG, JPEG 2000 and the more recently introduced Better Portable Graphics (BPG) algorithm, which is based on a subset of the High Efficiency Video Coding (HEVC) standard. We propose a HEVC-based multi-sample compression which takes advantage of inter-frame prediction to achieve a more compact storage of iris images. Experiments on cropped iris images of the IITDv1 and the CASIAv4-Interval datasets confirm the usefulness of the presented approach. Compared to a separate storage of multiple BPG encoded images of size 2 to 3 KB the required storage space can be reduced by at least 30% if images are acquired in a single session. Similarly, at constant file sizes a relative enhancement of image quality of at least 5% in terms of PSNR is achieved. Compared to the widely recommended JPEG 2000 compression, obtained performance gains become even more pronounced. Gains with respect to image quality are also reflected in experiments on recognition performance.