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Evaluating the Impact of Iris Image Compression on Segmentation and Recognition Accuracy

A comprehensive study of the effects of lossy image compression on iris biometrics is presented. The compression standards Jpeg (JPG), Jpeg-2000 (J2K) and Jpeg-XR (JXR) are applied in numerous specified scenarios utilizing different segmentation and feature extraction algorithms in order to investigate impacts on recognition accuracy. Augmenting existing evaluations, this work examines not only the optimal choice of compression algorithms and rates, but also emphasizes segmentation issues resulting from compressed images. In addition, the impact of image compression on template protection techniques is elaborated. Experimental results confirm, that (1) J2K outperforms JPG and JXR for compression prior to normalization, (2) the choice of where to employ compression in the iris processing chain plays an important role, as well as (3) whether one or both compared images are compressed; (4) for high compression rates, the impact on segmentation is most critical, and (5) despite the fact that template protection schemes are highly sensitive to signal degradation, compression can be successfully applied to such technologies.