Recompression effects in iris recognition |
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| Affiliation: | 1. Authentic Vision GmbH, Austria;2. Department of Computer Sciences, University of Salzburg, Austria;1. College of Information Engineering, Capital Normal University, Beijing, China;2. Beijing Advanced Innovation Center for Imaging Technology, Beijing, China;3. School of Computer Science, Beijing Institute of Technology, Beijing, China;1. Department of Electrical and Computer Engineering, Carnegie Mellon University, United States;2. Department of Electronics and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;1. Department of Automation, Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China;2. Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR), 138632, Singapore;3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;4. School of Computer Science Software Engineering, The University of Western Australia, Crawley, WA6009, Australia |
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| Abstract: | Rating a compression algorithms' performance is usually done in experimental studies, where researchers have frequently used JPEG pre-compressed data. It is not clear yet, if results of such compression experiments are reliable when conducted on pre-compressed data. To investigate this issue, we first study the impact of using pre-compressed data in iris segmentation and evaluate the relation between iris segmentation performance and general image quality metrics. In this context we propose a method to overcome potential problems in case using pre-compressed data sets cannot be avoided. As the second step, we conduct experimentation on the entire iris recognition pipeline. We find that overall, recognition accuracy results might not be entirely reliable in case of applying JPEG XR or JPEG2000 to JPEG pre-compressed data. |
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