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Effect of severe image compression on iris recognition performance
, 2007
"... Effect of severe image compression on iris recognition performance ..."
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Effect of severe image compression on iris recognition performance
Quantitative standardization of iris image formats
- in Proc. of the Biometrics and Electronic Signatures (BIOSIG 2009), Lecture
"... Abstract: This paper gives performance-based results for the application of a leading recognition algorithm applied to standardized iris imagery. The implementation was evaluated in NIST’s IREX program. The program was conducted in cooperation with the iris recognition industry to evaluated whether ..."
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Abstract: This paper gives performance-based results for the application of a leading recognition algorithm applied to standardized iris imagery. The implementation was evaluated in NIST’s IREX program. The program was conducted in cooperation with the iris recognition industry to evaluated whether standardized image formats can be interoperable and compact. This is required for federated applications in which iris data is exchanged between interoperating systems, passed across bandwidth-limited networks, or stored on identity credentials. The study gave quantitative support to the revision of the ISO/IEC 19794-6 standard. This paper shows the effect of compres-sion on false non-match rate and also, notably on false match rate also. The paper compares JPEG and JPEG 2000. 1
Evaluating the Impact of Iris Image Compression on Segmentation and Recognition Accuracy
, 2012
"... Abstract—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 ..."
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Abstract—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. Index Terms—Biometrics, iris recognition, image compression, iris segmentation, biometric template protection, Jpeg, Jpeg-2000, Jpeg-XR;
Jawaharlal Nehru Technological
"... In this research work, face recognition for the compressed images is evaluated when the images are stored at smaller sizes. When the images are stored at smaller sizes, the information is lost and sometimes it is not possible to recognize the faces successfully. The practical applications and need f ..."
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In this research work, face recognition for the compressed images is evaluated when the images are stored at smaller sizes. When the images are stored at smaller sizes, the information is lost and sometimes it is not possible to recognize the faces successfully. The practical applications and need for storing the images at smaller sizes is explained. The results for successful recognition are presented along with the eigen values and coefficients of the some of the eigen vectors. Finally important conclusions are drawn and future scope for the research is outlined. Index Terms—Face recognition, compression, image compression and face recognition, security. 1.
Face Recognition of Database of Compressed Images using Local Binary Patterns
"... Local binary pattern algorithm is used in this work to determine the recognition rate for the images stored in a compressed form in the database. The images are of two types, namely, probe image and the database images. Data base images are the one present in databases like airport servers, governme ..."
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Local binary pattern algorithm is used in this work to determine the recognition rate for the images stored in a compressed form in the database. The images are of two types, namely, probe image and the database images. Data base images are the one present in databases like airport servers, government servers etc., whereas the probe image is the one which is being tested against the database to find the matching picture or record from the database. In this work, the data base images are compressed on the size of the image by several compression levels and each level is tested for the same probe image. The probe image is not compressed while comparison. The simulation results are presented for the recognition rate under different levels of compression.
Effect of Severe Image Compression on Iris Recognition Performance
"... Abstract—We investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCo ..."
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Abstract—We investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available database of iris images. We show that it is possible to compress iris images to as little as 2000 bytes with minimal impact on recognition performance. Only some 2% to 3 % of the bits in the IrisCode templates are changed by such severe image compression, and we calculate the entropy per code bit introduced by each compression scheme. Error tradeoff curve metrics document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size. Index Terms—Biometrics, image compression, image segmentation, iris recognition, JPEG2000, region of interest, receiver operating characteristic (ROC) curves. I.
Iris Compression and Recognition using Spherical Geometry Image
"... Abstract—this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compres ..."
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Abstract—this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compression techniques used in 3D domain to test it and give an approximation results or to focus on this new direction in research. In this research a fully automated 3D iris compression and recognition system is presented. We use spherical based wavelet coefficients for efficient representation of the 3D iris. The spherical wavelet transformation is used to decompose the iris image into multi-resolution sub images. The representation of features based on spherical wavelet parameterization of the iris image was proposed for the 3D iris compression system. To evaluate the performance of the proposed approach, experiments were performed on the CASIA Iris database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the iris image was found to generate good recognition results.
