The Experts below are selected from a list of 11307 Experts worldwide ranked by ideXlab platform
Hiroshi Nakajima - One of the best experts on this subject based on the ideXlab platform.
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a phase based Iris Recognition algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Kazuyuki Miyazawa, Takafumi Aoki, Koji Kobayashi, Hiroshi NakajimaAbstract:This paper presents an efficient algorithm for Iris Recognition using phase-based image matching. The use of phase components in two-dimensional discrete Fourier transforms of Iris images makes possible to achieve highly robust Iris Recognition with a simple matching algorithm. Experimental evaluation using the CASIA Iris image database (ver. 1.0 and ver. 2.0) clearly demonstrates an efficient performance of the proposed algorithm.
Shoba Krishnan - One of the best experts on this subject based on the ideXlab platform.
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ICWET - Image compression and its effect on Iris Recognition
Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET '11, 2011Co-Authors: A. Birje, Shoba KrishnanAbstract:A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris Recognition is one of techniques used to identify people. Commercial Iris Recognition systems are currently employed to allow passengers in some airports to be rapidly processed through security, to allow access to secure areas, and for secure access to computer networks. With the growing employment of Iris Recognition systems and associated research to support this, the need for large databases of Iris images is growing. If required storage space is not adequate for these images, compression is an alternative. It allows a reduction in the space needed to store these Iris images, although it may the cost of some amount of information lost in the process and therefore the solution is Image Compression. This work investigates the effects of image compression on Iris Recognition. Compression is performed using SPIHT and JPEG2000, and the Iris Recognition algorithm used is based on Daugman's methods.
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ICWET - Iris Recognition with image compression using SPIHT
Proceedings of the International Conference and Workshop on Emerging Trends in Technology - ICWET '10, 2010Co-Authors: Archana J. Gawad, Shoba KrishnanAbstract:Iris Recognition is used to identify people. A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Commercial Iris Recognition systems are used, to allow access to secure areas, and for secure access to computer networks. With the growing employment of Iris Recognition systems and associated research to support this, the need for large databases of Iris images is growing. If required storage space is not adequate for these images, compression is an alternative. It allows a reduction in the space needed to store these Iris images, although it may be at a cost in some amount of information lost in the process. This paper investigates the effects of image compression on Iris Recognition. Compression is performed using SPIHT, and the Iris Recognition algorithm used is based on several methods.
Yingzi Du - One of the best experts on this subject based on the ideXlab platform.
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region based sift approach to Iris Recognition
Optics and Lasers in Engineering, 2009Co-Authors: Craig Belcher, Yingzi DuAbstract:Traditional Iris Recognition systems transfer Iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an Iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative Iris Recognition systems are capable of correctly matching these 2-D representations of Iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of Iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative Iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for Iris Recognition because it does not take advantage of the characteristics of Iris patterns. We propose the region-based SIFT approach to Iris Recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform Iris Recognition and is scale invariant. This method was tested on the Iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative Iris Recognition.
Kazuyuki Miyazawa - One of the best experts on this subject based on the ideXlab platform.
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a phase based Iris Recognition algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Kazuyuki Miyazawa, Takafumi Aoki, Koji Kobayashi, Hiroshi NakajimaAbstract:This paper presents an efficient algorithm for Iris Recognition using phase-based image matching. The use of phase components in two-dimensional discrete Fourier transforms of Iris images makes possible to achieve highly robust Iris Recognition with a simple matching algorithm. Experimental evaluation using the CASIA Iris image database (ver. 1.0 and ver. 2.0) clearly demonstrates an efficient performance of the proposed algorithm.
Yunhong Wang - One of the best experts on this subject based on the ideXlab platform.
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robust encoding of local ordinal measures a general framework of Iris Recognition
Lecture Notes in Computer Science, 2004Co-Authors: Yunhong WangAbstract:The randomness of Iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex Iris image structure and various sources of intra-class variations result in the difficulty of Iris representation. Although diverse Iris Recognition methods have been proposed, the fundamentals of Iris Recognition have not a unified answer. As a breakthrough of this problem, we found that several accurate Iris Recognition algorithms share a same idea — local ordinal encoding, which is the representation well-suited for Iris Recognition. After further analysis and summarization, a general framework of Iris Recognition is formulated in this paper. This work discovered the secret of Iris Recognition. With the guidance of this framework, a novel Iris Recognition method based on robust estimating the direction of image gradient vector is developed. Extensive experimental results demonstrate our idea.
