Deep Iris Compression
| Autor: | Ehsaneddin Jalilian, Heinz Hofbauer, Andreas Uhl |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Iris recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY computer.file_format Lossy compression Iris flower data set JPEG WebP JPEG 2000 Computer vision Artificial intelligence business computer Image compression Data compression |
| Zdroj: | Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030688202 ICPR Workshops (5) |
| DOI: | 10.1007/978-3-030-68821-9_40 |
| Popis: | Lossy image compression can reduce the space and bandwidth required for image storage and transmission, which is increasinly in demand by the iris recognition systems developers. Deep learning techniques (i.e. CNN, and GAN networks) are quickly becoming a tool of choice for general image compression tasks. But some key quality criteria, such as high perceptual quality and the spatial precision of the images, need to be satisfied when applying such modules for iris images compression tasks. We investigate and evaluate the expediency of a deep learning based compression model for iris data compression. In particular, we relate rate-distortion performance as measured in PSNR, and Multi-scale Structural Similarity Index (MS-SSIM) to the recognition scores as obtained by a concrete recognition system. We further compare the model performance against a state-of-the-art deep learning base image compression technique as well as some lossy compression algorithms currently used for iris compression (namely: the current ISO standard JPEG2000, JPEG, H.265 derivate BPG, and WEBP), to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression, and promising recognition performance of the model over all other techniques on different iris data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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