Iris Pattern

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V. Mohan - One of the best experts on this subject based on the ideXlab platform.

  • Biometric personal identification based on Iris Pattern recognition using Wavelet Packet Transform
    2010 Second International conference on Computing Communication and Networking Technologies, 2010
    Co-Authors: S. Hariprasath, V. Mohan
    Abstract:

    A new Iris recognition system based on Wavelet Packet Analysis and Morlet wavelet is described. Morlet wavelet calculations are easy compared to Gabor wavelets. Moreover Gabor wavelet based Iris recognition system is patented which blocks its further development. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's Iris. The visible texture of a person's Iris is encoded into a compact sequence of 2-D Morlet wavelet coefficients, which generate an “Iris code” of 4096-bits. Two different Iris codes are compared using exclusively OR comparisons. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for Iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of Iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or -1 as Iris signature. This signature presents the local information of different Irises. The signature of the new Iris Pattern is compared against the stored Pattern after computing the signature of new Iris Pattern and identification is performed.

  • Iris Pattern RECOGNITION USING COMPLEX WAVELET AND WAVELET PACKET TRANSFORM
    2009
    Co-Authors: V. Mohan
    Abstract:

    A new Iris recognition system based on Wavelet Packet Analysis and Morlet wavelet is described. Morlet wavelet calculations are easy compared to Gabor wavelets. Moreover Gabor wavelet based Iris recognition system is patented which blocks its further development. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s Iris. The visible texture of a person’s Iris is encoded into a compact sequence of 2-D Morlet wavelet coefficients, which generate an “Iris code” of 2025-bits. Two different Iris codes are compared using exclusively OR comparisons. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for Iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of Iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or -1 as Iris signature. This signature presents the local information of different Irises. The signature of the new Iris Pattern is compared against the stored Pattern after computing the signature of new Iris Pattern and identification is performed.

  • Biometric personal identification based on Iris recognition using complex wavelet transforms
    2008 International Conference on Computing Communication and Networking, 2008
    Co-Authors: S. Hariprasath, V. Mohan
    Abstract:

    A new Iris recognition system based on complex wavelet Transforms is described. In this work Iris recognition based on Gabor wavelet and Morlet wavelet are described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's Iris. The visible texture of a person's Iris is encoded into a compact sequence of 2-D wavelet coefficients, which generate an ldquoIris coderdquo of 4096-bits. The statistical parameters like mean and covariance of coefficients of the Iris images are also computed. Two different Iris codes are compared using exclusively OR comparisons. Also the new Iris Pattern is compared against the stored Pattern after computing the probability of new Iris Pattern and identification is performed.

S. Hariprasath - One of the best experts on this subject based on the ideXlab platform.

  • bimodal biometric Pattern recognition system based on fusion of Iris and palmprint using multi resolution approach
    Signal Image and Video Processing, 2020
    Co-Authors: S. Hariprasath, M Santhi
    Abstract:

    A biometric Pattern recognition system is a Pattern recognition system which uses the biological traits to recognize individuals. Single biometric systems may not possess the required characteristics like permanence, acceptability and circumvention. In general, the performance of the unimodal biometrics systems is degraded due to noisy data acquisition from sensor, intra-class and inter-class similarities. Several such restrictions are removed when multiple sources of information are used. In this paper, a bimodal biometric system designed from Iris Pattern and palmprint Pattern is described. The feature extractor is created from the wavelet packet analysis, and classifier is created based on neural network. Using wavelet packets and gray-level spatial dependence matrix, the Iris code vector is constructed. With the help of Gabor wavelets and gray-level spatial dependence matrix, the palmprint code vector is computed. The features extracted from Iris Pattern and palmprint Pattern are fused by feature-level fusion into a multimodal Pattern vector of size 1408 bits. The recognition rate achieved by the LVQ neural network is 94.50%. This system can complete recognition in 15.25 microseconds so that it can be made use for implementing real-time recognition tasks.

  • Biometric personal identification based on Iris Pattern recognition using Wavelet Packet Transform
    2010 Second International conference on Computing Communication and Networking Technologies, 2010
    Co-Authors: S. Hariprasath, V. Mohan
    Abstract:

    A new Iris recognition system based on Wavelet Packet Analysis and Morlet wavelet is described. Morlet wavelet calculations are easy compared to Gabor wavelets. Moreover Gabor wavelet based Iris recognition system is patented which blocks its further development. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's Iris. The visible texture of a person's Iris is encoded into a compact sequence of 2-D Morlet wavelet coefficients, which generate an “Iris code” of 4096-bits. Two different Iris codes are compared using exclusively OR comparisons. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for Iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of Iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or -1 as Iris signature. This signature presents the local information of different Irises. The signature of the new Iris Pattern is compared against the stored Pattern after computing the signature of new Iris Pattern and identification is performed.

  • Iris Pattern recognition using biorthogonal Wavelet Packet Analysis
    2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES, 2010
    Co-Authors: S. Hariprasath, S. Venkatasubramaniam
    Abstract:

    A new Iris recognition system based on Wavelet Packet Analysis is described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's Iris. The visible texture of a person's Iris is encoded into a compact sequence of 2-D wavelet packet coefficients, which generate an “Iris code”. Two different Iris codes are compared using exclusively OR comparisons. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for Iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of Iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or −1 as Iris signature. This signature presents the local information of different Irises. By using different wavelet packets the size of the Iris signature of code attained was 1080 bits. The signature of the new Iris Pattern is compared against the stored Pattern after computing the signature of new Iris Pattern. Identification is performed by computing the hamming distance.

  • Biometric personal identification based on Iris recognition using complex wavelet transforms
    2008 International Conference on Computing Communication and Networking, 2008
    Co-Authors: S. Hariprasath, V. Mohan
    Abstract:

    A new Iris recognition system based on complex wavelet Transforms is described. In this work Iris recognition based on Gabor wavelet and Morlet wavelet are described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's Iris. The visible texture of a person's Iris is encoded into a compact sequence of 2-D wavelet coefficients, which generate an ldquoIris coderdquo of 4096-bits. The statistical parameters like mean and covariance of coefficients of the Iris images are also computed. Two different Iris codes are compared using exclusively OR comparisons. Also the new Iris Pattern is compared against the stored Pattern after computing the probability of new Iris Pattern and identification is performed.

Dibyendu Ghoshal - One of the best experts on this subject based on the ideXlab platform.

  • study of Iris Pattern matching and detection of the persons having squint
    Indian journal of science and technology, 2016
    Co-Authors: Dibyendu Ghoshal
    Abstract:

    Objectives: The present paper has attempted a quantitative study to measure the amount of off centricity of the Iris from its normal position. Methods/Statistical Analysis: The eye image having squint has been taken from real life image (CASIA DATABASE). First of all the central position of the eye opening between the eyelids are detected by Matlab code. Then the distance between the locations of the squint eye are taken by using 3X3 spatial filter with a local searching method. Findings: Squint is the off centre location of Iris and pupils and may occur either in one eye or both of the eyes and both male and female persons may have squint. The local searching method terminates when the centre of the filter coincides with the centre of the pupil of the squint eye. The filters are moved from the centre of the eye by coinciding the centre of the spatial filter with the centre of the eye in vertical, horizontal and angular directions. Application/Improvement: By this process noises will be reduced which are prevalent in squint eye. The number of pixels responsible for the exact locations may be compensated by making the successive position of the filters bit overlapping which will in turn reduce the possibility of having checker board appearance in the output of the image.

  • Iris Pattern Recognition of the Kingfisher Bird using Discrete Wavelet Transform and Feature Extraction from Histogram Orientation Gradient
    Indian Journal of Science and Technology, 2016
    Co-Authors: Dibyendu Ghoshal
    Abstract:

    Objectives: The present study has dealt with a new idea for recognizing the Iris Pattern and their matching of the Kingfisher, a colorful bird. Methods/Statistical Analysis: The investigation has been made to detect, recognize and match the Irises through a series of process. The work is centered about the improvement of the matrices concerned; rather than reducing the execution time of the program. For the present paper, MATLAB R2016a is used as the development tool. Findings: The proposed work is expected to give a recognition rate of 93% with a false rejected rate of 1.0. Application/Improvement: The results may be used for the census of Kingfisher birds and also towards wildlife preservation.

  • Dual Authentication of a Human Being from Simultaneous Study of Palm Pattern and Iris Recognition
    Indian Journal of Science and Technology, 2016
    Co-Authors: Dibyendu Ghoshal, Tapajyoti Deb
    Abstract:

    Objective: The present study deals with a dual authentication of a human being from simultaneous analysis of Iris Pattern and single side of the palm of same person which is expected to form some basis in the dual biometric based authentication to avoid fraudulent activities. Methods/Statistical Analysis: For Iris Pattern matching, a thorough denoising by LOG Gabor filter of 7X7 dimension is used. Subsequently edge linking and Iris boundary detection is carried out by local processing. For palm detection, in the current study the features of front portion of the palm are considered. The tool that will be used for the development purpose is MATLABR2016a, and emphasis will only be on the software for performing recognition. Findings: In the front side, the various mounts, creases, some deep uneven spaces and wrinkles in the human wrist are evaluated. The recognition rate for the human Iris Pattern has been observed to be around 95.5% while that of palm has yielded 91.99%. Application/Improvement: The aim of the study is to provide better social security and proper identification i.e. authentication of a particular person especially when one biometric is lost due to some severe accident.

  • Recognition of Non Circular Iris Pattern of the Goat by Structural, Statistical and Fourier Descriptors
    Procedia Computer Science, 2016
    Co-Authors: Dibyendu Ghoshal
    Abstract:

    Abstract The present paper has described a comparative study to find the Iris Pattern of the goat which has nearly rectangular or square type appearance. For detecting the structural descriptors, the deviation of the Iris Pattern shape and size from a standard circular (annular) shaped have been thoroughly studied. Statistical feature extraction has mainly dealt with the various types of moments e.g. – mean variance skewness and kurtosis1, 2. Fourier descriptors have been extracted by 2D Fourier Transformation of the entire data set comprising Patterns. It has been found that Fourier Descriptors are not directly insensitive to possible geometrical changes of the Iris location like translation, rotation and scale change occurring due to eye ball movement and blinking of the eye lids. The result shows that the structural descriptors based Pattern recognition rate produce a recognition rate of 97.85% with 4.5% of false acceptance rate and 2.2% false rejection rate. The images during the study were acquired from real life with 16 megapixel camera resolution.

  • Identification of Tigers for Census by the Method of Tiger Iris Pattern Matching and Recognition
    International Journal of Computer Applications, 2012
    Co-Authors: Dibyendu Ghoshal, Bapi Saha
    Abstract:

    The Iris Pattern of any animal (including human being) is statistically unique and suitable for biometric measurements. The identity of the animal concerned can be determined and verified comparing the templates obtained with the present algorithm with that template stored in database which was formed on the basis of previous studies. In the present study, the method of circular Hough transform is used for segmentation of the tiger Iris and subsequently Daugman‘s rubber Sheet model is used for normalization of the segmented values. Pattern matching is achieved by calculating Hamming Distance where its degree is proportional to the closeness of matching. The closer matching between the stored and calculated Pattern is found to lead towards better recognition of Irises and thereby the animal itself.

Shaila Subbaraman - One of the best experts on this subject based on the ideXlab platform.

  • SVD-EBP Algorithm for Iris Pattern Recognition
    arXiv: Computer Vision and Pattern Recognition, 2012
    Co-Authors: Babasaheb G. Patil, Shaila Subbaraman
    Abstract:

    This paper proposes a neural network approach based on Error Back Propagation (EBP) for classification of different eye images. To reduce the complexity of layered neural network the dimensions of input vectors are optimized using Singular Value Decomposition (SVD). The main of this work is to provide for best method for feature extraction and classification. The details of this combined system named as SVD-EBP system, and results thereof are presented in this paper. Keywords- Singular value decomposition(SVD), Error back Propagation(EBP).

  • SVD-EBP Algorithm for Iris Pattern Recognition
    International Journal of Advanced Computer Science and Applications, 2011
    Co-Authors: Babasaheb G. Patil, Shaila Subbaraman
    Abstract:

    This paper proposes a neural network approach based on Error Back Propagation (EBP) for classification of different eye images. To reduce the complexity of layered neural network the dimensions of input vectors are optimized using Singular Value Decomposition (SVD). The main objective of this work is to prove usefulness of SVD to form a compact set of features for classification by EBP algorithm. The results of our work indicate that optimum classification values are obtained with SVD dimensions of 20 and maximum number of classes as 9 with the state-of-the art computational resources The details of this combined system named as SVD-EBP system for Iris Pattern recognition and the results thereof are presented in this paper.

  • human Iris Pattern recognition using phase components of image
    International Conference on Industrial and Information Systems, 2009
    Co-Authors: Babasaheb G. Patil, Shaila Subbaraman
    Abstract:

    The increase in security concerns on issues such as identity, theft indicates the needs of a new reliable security system. Iris recognition is perhaps the most accurate means of personnel identification due to the uniqueness of the Patterns contained in each Iris. This paper presents new image processing algorithm which specifically focuses on the characteristics of the phase components obtained from two-dimensional Fourier Transformation of image. The Phase Only Correlatlon (POC) and Band Limited Phase Only Correlatlon (BLPOC) are the most fundamental transformations, the features of which include superior discrimination capability over the ordinary recognition system.

S. A. Ladhake - One of the best experts on this subject based on the ideXlab platform.

  • neural network based Iris Pattern recognition system using discrete walsh hadamard transform features
    Advances in Computing and Communications, 2013
    Co-Authors: Mrunal M. Khedkar, S. A. Ladhake
    Abstract:

    An Iris Pattern recognition system is developed for CASIA database using discrete 2D Walsh Hadamard (WH) transform as a tool for feature extraction. Experimental prototype Pattern recognition (PR) system is designed in which Iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, texture and 2D WH transform domain. Based upon these features, an optimal feature vector comprising of only 23 features is selected and it is given as input to neural network based Pattern Recognition (PR) system. Two different neural network configurations including Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and a different learning machine, known as Support Vector Machine (SVM) are investigated for their suitability as a PR system. It is observed that MLP neural network based PR system comprising of only one hidden layer containing 20 processing elements (PEs) outperforms others in respect of performance measures on cross-validation (CV) dataset.

  • Robust human Iris Pattern recognition system using neural network approach
    2013 International Conference on Information Communication and Embedded Systems (ICICES), 2013
    Co-Authors: Mrunal M. Khedkar, S. A. Ladhake
    Abstract:

    This paper proposes a prototype model of robust Iris Pattern recognition (PR) system for classification of ten different persons using neural network. Feature extraction algorithms are developed and an optimal feature vector comprising of features in relation to image statistics, texture and 2-D transform domain is formed. It is observed that 2D Walsh Hadamard Transform (WHT) entails the best performance as compared to other image transforms. Different neural network configurations, such as, Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are implemented after systematically varying the concerned parameters of the respective networks and MLP with single hidden layer is seen to outperform all others with respect to performance on cross validation dataset derived from CASIA Iris image database. Further, the sensitivity analysis is carried out over this network in order to reduce time and space complexity of the network. To make this network more robust; controlled Gaussian and Uniform noise are injected in all input features and it is noticed that for both types of noise, the proposed PR system can sustain the variance up to 0.1.

  • ICACCI - Neural network based Iris Pattern recognition system using discrete Walsh Hadamard transform features
    2013 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2013
    Co-Authors: Mrunal M. Khedkar, S. A. Ladhake
    Abstract:

    An Iris Pattern recognition system is developed for CASIA database using discrete 2D Walsh Hadamard (WH) transform as a tool for feature extraction. Experimental prototype Pattern recognition (PR) system is designed in which Iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, texture and 2D WH transform domain. Based upon these features, an optimal feature vector comprising of only 23 features is selected and it is given as input to neural network based Pattern Recognition (PR) system. Two different neural network configurations including Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and a different learning machine, known as Support Vector Machine (SVM) are investigated for their suitability as a PR system. It is observed that MLP neural network based PR system comprising of only one hidden layer containing 20 processing elements (PEs) outperforms others in respect of performance measures on cross-validation (CV) dataset.