As a standard, 512 byte IrisCode templates developed with specific algorithms are stored in databases and used in iris recognition process. Future tendencies are to use exclusively real iris images rather than IrisCode templates in the iris recognition process. Many of current iris recognition systems use portable devices (e.g. iris scanners) which are often required to transmit image or template over communication channel. Image compression can be used in order to reduce the transmission time and storage capacities. Classified Vector Quantization (CVQ) and ordinary Vector Quantization (VQ) are used for compression of greyscale iris images collected from one of the available public databases of iris images. Results show that both compression methods are significantly more effective when applied to iris images than when applied to average images from everyday environments since iris images are fairly uniform and contain low
contrast levels. Originally, CVQ is used to improve the quality of edges of compressed images because they are the most important part of image for visual impression on humans. The paper presents the comparison and major advantage of CVQ over ordinary VQ in terms of significant time reduction needed for iris images to be coded, and therefore it highlights a new important application of CVQ.
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