# Fast identification of individuals based on iris characteristics for biometric systems

Size: px
Start display at page:

Download "Fast identification of individuals based on iris characteristics for biometric systems"

## Transcription

2 being sought and r as radius. The parameter space is discretized and represented as an array of integers or cells, where each position in the array corresponds to a range of parameters in the real space. Wanted all circles (a, b, r) passing through each point (x, y). Figure the iris has been limited by the Daugman operator. r ( x a) ( y b) (1) Figure 1 shows an example of using the Hough Transform, where Figure 1(a) is the original image, obtained from segmenting one image of a human eye, and Figure 1(b) is the image obtained after using the Hough Transform, i.e. the circle detected. Figure. - Image of an iris located by the method of Daugman [1]. 3. FAST IRIS LOCATION Figure 1 Example of the application of the Hough Transform: (a) segmented image of a human eye, (b) circle detected by the Hough Transform. As can be seen in Figure 1, the Hough transform, when associated with an efficient segmentation method can produce good results in the identification of the iris in images... Integro-Differential Operator Another method widely referenced for the localization of the iris in images is the Integro- Differential operator, proposed by Daugman[1]. This operator is given by the equation. max G r ( r) * r, x0, y0 ds () ( r, xo, y0 ) I( x, y) r where, I (x, y) is the image containing the eye to be analyzed, r is the radius and x0, y0 are the coordinates of the iris center. In this equation, the symbol * denotes the image convolution and is a function of smoothing with a Gaussian filter of scale σ. Looking over the image domain by the maximum value of the partial derivative with respect to the radius r, the normalized integral of the contour of the image along a circular arc ds [1]. According to Daugman[1], with this technique it is possible to estimate separately the parameters of the iris and pupil, delimiting the inner contour of the iris with the pupil and the outer with the sclera. In The identification process consists in the image segmentation to separate the region of interest of the iris. This work proposes to use only the inner region of the iris, discarding the outer region. Two reasons led to this decision: 1)The internal region of the iris, closest to the pupil, is one that concentrates most of the specific features of every human being, while the external region, nearer the sclera, has a smaller number of specific features [8]. )The process of segmentation of the iris is usually one of the most computational expensive steps of the common recognition process. The use only the inner region of the iris reduces the processing time for separating the region of interest without losing the most important features for the iris recognition. In this work, due to using only the inner region of the iris, is not necessary the location of the boundary between the iris and sclera. Targeting the boundary between the pupil and the iris is possible to separate the region of interest for the achievement of the following processes. As the pupil has lower intensity than the rest of the image, we can use the histogram equalization followed by thresholding and a sequence of morphological operations to perform the segmentation of the boundary between the pupil and iris. The use of morphological operations instead of edge detection operators makes the segmentation process faster, which is highly relevant when one intends to use the iris recognition system in real time. The sequence of talks used in the segmentation process of the pupil are: 1) histogram equalization, which aims to achieve a better distribution of the gray levels over the input image, causing a higher differentiation of the intensities presented; ) thresholding the equalized image, aiming to separate

3 the image regions with gray levels below a certain value, by using as threshold the average value obtained from the pixels in the central region of the image; 3) dilation, used to remove potential sources of low intensity that are not part of the pupil; 4) opening, which aims to repair possible faults in the region of the pupil caused by applying the dilation operator; and 5) extraction of borders, used to let the edges of the image. Figure 3 presents an example of applying these tasks to an original image: Figure 3(a) corresponds to the original image; Figure 3(b) shows the image after histogram equalization; Figure 3(c) presents the thresholded image; Figure 3(d) has the image after dilatation; Figure 3(e) shows the image after applying the open operator; Figure 3(f) presents the image after the extraction operation of the borders; and Figure 3(g) shows the original image overlapped with the pupil identified. It should be noted that the circle visible on Figure 3(g) resulted from the last morphological operation that extracted the border presented in Figure 3(f). the spatial resolution of the used images. Tests were conducted using fixed distances between 0 pixels and 80 pixels, being 50 the number of pixels for this distance that led to the best results during the recognition process. 3. Region of interest defined through a percentage inversely proportional to the distance from the border of the iris with the pupil to the border of the iris with the sclera. The number of pixels was kept approximately constant during comparisons, disregarding eventual dilation or contraction of the pupil. After applying some tests on the database, we decided to use the fixed distance approach to locate the region of interest, as this one presented the best results. Such results are presented in section 4. Figure 4 shows an example of the region of interest located in the iris. Figure 4 - Identification of the iris region of interest: (a) image with the pupil identified, (b) identified region of interest of the iris. Figure 3 - Process of pupil identification: (a) original image, (b) image after histogram equalization, (c) thresholded image (d) image after the dilation, (e) image after opening operation, (f) image after the borders operation, (g) original image overlapped with the detected circle of the iris. After locating the pupil, we must find the region of interest of the iris for further recognition. Having the outline of the pupil, the process of separating the region of interest of the iris becomes simpler. Three different approaches were taken for testing the definition of the iris region of interest, as follows: 1. Region of interest defined by a distance corresponding to the length of the radius of the pupil. Such a distance has been circularly applied all over the iris from its border with the pupil so defining the iris region of interest.. Region of interest defined by a distance corresponding to a fixed amount of pixels. Such a distance has been circularly applied all over the iris from its border with the pupil. In this case the distance has been empirically determined from the analysis of the image database to be 50 pixels, as this distance must be proportional to 4. TESTS AND RESULTS For the experimental tests, we used the images of the iris database of the Chinese Academy of Sciences - Institute of Automation (CASIA). The choice of this image database is due to the fact that it has been used for testing in the works used in this work for comparison purpose. We also used two different computational platforms during the tests: Machine I: Personal computer with Intel Pentium 4 processor, at.4 GHz, and 56 Mb RAM, using Microsoft Windows XP and Matlab version 6.1. Machine II: Notebook with Intel I3 processor, at.4 GHz, and 4 GB of RAM, using Microsoft Windows 7 and Matlab version 7.8. To locate the region of interest of the iris three different models were tested, as described in section 3. Table 1 displays the results obtained from the three models with respect to the localization of the region of interest accuracy ratio. It can be seen that the model based on a fixed region around the pupil presented the best results, thus being used as the method of choice for the present work.

4 The worst results presented by the model based on the length of the radius of the pupil are related to cases were the pupil is too dilated, in which case the radius of the pupil is larger than the radius of the iris. In such occurrences, the method takes regions of the sclera as regions of interest of the iris. The worst results obtained from the method that takes a percentage of the whole iris are due to the difficulty in finding the border between the iris and the sclera. In this case the border is incorrectly guessed for several of the images. Table 1. Accuracy in iris localization Method Accuracy (%) 1. Length of the radius of the pupil Fixed amount of pixels Percentage inversely proportional 8.75 The location of the region of interest of the iris represented in an eye image is one of the most complex processes in recognition of people through the iris and, without doubt, the process that requires the highest computational cost. The reduction of the recognition area of the iris, as reported in section 3, gives to the proposed algorithm a high gain with respect to the time necessary to carry out the recognition process. Moreover, the need for detecting only the border between the iris and pupil makes the localization relatively simple and provides a high accuracy. Table shows a comparison between the accuracy rates in localizing the iris in the image of the database used between the proposed method and other methods used as reference. It can be observed that the proposed method is the only that had localized successfully the region of interest in all testing images. Table. Comparison between the accuracy rates in localizing the iris Method Accuracy (%) Proposed Wildes[4] Daugman[1] Masek[3] 8.53 Table 3 presents the comparison in terms of the average computational time required to locate the region of interest in the testing images. We can verify that the proposed method requires a time lower than the other methods for locating the region of interest of the iris, which is due to a decrease in the region to be analyzed. It should be noted the high computational time required by the Masek method. This method uses as a basis for the exact location of the iris the Hough Transform, which despite being proven effective for locating circles in images, is very computationally expensive. Table 3. Segmentation processing time. Method Time (s) Proposed 0.8 Wildes[4] 1.98 Daugman[1] 6.56 Masek[3] 3.37 The times shown in Table 3 were obtained using Machine I. Performing the test of the proposed method using the Machine II, the average computational was 0.7 seconds. 5. CONCLUSION The recognition of individuals based on biometric techniques has gained great prominence in recent years. Among these techniques the one that presents the most interesting results is the recognition of individuals through iris features. However, most existing methods have the drawback of high computational costs, mainly because segmentation of the iris from the rest of the input image is needed. In this paper we presented a method in which the segmentation process can be performed with minimal computational cost, and that could identify successfully all the irises in the testing image database used. We could verify that the proposed method is very promising, primarily for its excellent performance with respect to the required computational effort and especially for not failing to identify the region of interest in all of the testing images used. In spite of the main goal of the described work having been the speed up of the iris segmentation process, it should be noted an important outcome that was also obtained, which may lead to a promising future research: using only the region of the iris nearest to the pupil, a 99.4% level of accuracy over the entire image database as achieved. ACKNOWLEDGMENTS The authors are thankful to FUNDUNESP Fundação para o Desenvolvimento da UNESP Brazil and FAPESP Fundação de Amparo à Pesquisa do Estado de São Paulo Brazil for the financial support. REFERENCES [1] J. Daugman. How Iris Recognition Works. IEEE Trans. on Circuits and Systems for Video Technology, 14(1)1-30, 004. [] A. C. Gonzales e R. E. Woods. Digital Image Processing 3ª Edition. Pearson Prentice Hall, São Paulo, Brazil, 010.

5 [3] L. Masek. Recognition of Human Iris Patterns for Biometric Identification. Master s Degree Dissertation. School of Computer Science and Software Engineering, The University of Western, Australia, 003. [4] R. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9) , [5] G. D. Duarte. Use of the Hough Transform to detect circles in digital images. Available accessed in september of 010. [6] L. Ma, T. Tan, Y. Wang e D. Zhang. Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence,5(1) , 003. [7] J. Daugman. Wavelet Demodulation Codes, Statistical Independence, and Pattern Recognition. Available at: citeseerx.ist.psu.edu/viewdoc/download?doi= pdf, acessed in september 010. [8] M. Pereira. A Proposal to the Reliability Increase of Iris Recognition System and its Implementation via Genetic Algorithms. Master s Degree Dissertation. Department of Eletrical Engineering, University of Uberlândia [9] Cui, Y. Wang, T. Tan, L. Ma and Z. Sun. A Fast and Robust Iris Localization Method Based on Texture Segmentation. Center for Biometric Authentication and Testing, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, P. R. China, 004. [10] L. Ma, T. Tan, Y. Wang and D. Zhang. Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing, 13(13) , 004.

### Iris Segmentation & Recognition in Unconstrained Environment

www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue -8 August, 2014 Page No. 7514-7518 Iris Segmentation & Recognition in Unconstrained Environment ABSTRACT

### Experiments with An Improved Iris Segmentation Algorithm

Experiments with An Improved Iris Segmentation Algorithm Xiaomei Liu, Kevin W. Bowyer, Patrick J. Flynn Department of Computer Science and Engineering University of Notre Dame Notre Dame, IN 46556, U.S.A.

### Iris Recognition using Hamming Distance and Fragile Bit Distance

IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 06, 2015 ISSN (online): 2321-0613 Iris Recognition using Hamming Distance and Fragile Bit Distance Mr. Vivek B. Mandlik

### An Efficient Approach for Iris Recognition by Improving Iris Segmentation and Iris Image Compression

An Efficient Approach for Iris Recognition by Improving Iris Segmentation and Iris Image Compression K. N. Jariwala, SVNIT, Surat, India U. D. Dalal, SVNIT, Surat, India Abstract The biometric person authentication

### Iris Recognition using Histogram Analysis

Iris Recognition using Histogram Analysis Robert W. Ives, Anthony J. Guidry and Delores M. Etter Electrical Engineering Department, U.S. Naval Academy Annapolis, MD 21402-5025 Abstract- Iris recognition

### NOVEL APPROACH OF ACCURATE IRIS LOCALISATION FORM HIGH RESOLUTION EYE IMAGES SUITABLE FOR FAKE IRIS DETECTION

International Journal of Information Technology and Knowledge Management July-December 2010, Volume 3, No. 2, pp. 685-690 NOVEL APPROACH OF ACCURATE IRIS LOCALISATION FORM HIGH RESOLUTION EYE IMAGES SUITABLE

### ANALYSIS OF PARTIAL IRIS RECOGNITION

ANALYSIS OF PARTIAL IRIS RECOGNITION Yingzi Du, Robert Ives, Bradford Bonney, Delores Etter Electrical Engineering Department, U.S. Naval Academy, Annapolis, MD, USA 21402 ABSTRACT In this paper, we investigate

### International Conference on Innovative Applications in Engineering and Information Technology(ICIAEIT-2017)

Sparsity Inspired Selection and Recognition of Iris Images 1. Dr K R Badhiti, Assistant Professor, Dept. of Computer Science, Adikavi Nannaya University, Rajahmundry, A.P, India 2. Prof. T. Sudha, Dept.

### A Proficient Matching For Iris Segmentation and Recognition Using Filtering Technique

A Proficient Matching For Iris Segmentation and Recognition Using Filtering Technique Ms. Priti V. Dable 1, Prof. P.R. Lakhe 2, Mr. S.S. Kemekar 3 Ms. Priti V. Dable 1 (PG Scholar) Comm (Electronics) S.D.C.E.

### Research on Pupil Segmentation and Localization in Micro Operation Hu BinLiang1, a, Chen GuoLiang2, b, Ma Hui2, c

3rd International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2015) Research on Pupil Segmentation and Localization in Micro Operation Hu BinLiang1, a, Chen GuoLiang2,

### Note on CASIA-IrisV3

Note on CASIA-IrisV3 1. Introduction With fast development of iris image acquisition technology, iris recognition is expected to become a fundamental component of modern society, with wide application

### IRIS Recognition Using Cumulative Sum Based Change Analysis

IRIS Recognition Using Cumulative Sum Based Change Analysis L.Hari.Hara.Brahma Kuppam Engineering College, Chittoor. Dr. G.N.Kodanda Ramaiah Head of Department, Kuppam Engineering College, Chittoor. Dr.M.N.Giri

### Content Based Image Retrieval Using Color Histogram

Content Based Image Retrieval Using Color Histogram Nitin Jain Assistant Professor, Lokmanya Tilak College of Engineering, Navi Mumbai, India. Dr. S. S. Salankar Professor, G.H. Raisoni College of Engineering,

### VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL

VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) prasannaven.palani@mavs.uta.edu

### Teaching Scheme. Credits Assigned (hrs/week) Theory Practical Tutorial Theory Oral & Tutorial Total

Code ITC7051 Name Processing Teaching Scheme Credits Assigned (hrs/week) Theory Practical Tutorial Theory Oral & Tutorial Total Practical 04 02 -- 04 01 -- 05 Code ITC704 Name Wireless Technology Examination

### Detection of License Plates of Vehicles

13 W. K. I. L Wanniarachchi 1, D. U. J. Sonnadara 2 and M. K. Jayananda 2 1 Faculty of Science and Technology, Uva Wellassa University, Sri Lanka 2 Department of Physics, University of Colombo, Sri Lanka

### Authentication using Iris

Authentication using Iris C.S.S.Anupama Associate Professor, Dept of E.I.E, V.R.Siddhartha Engineering College, Vijayawada, A.P P.Rajesh Assistant Professor Dept of E.I.E V.R.Siddhartha Engineering College

### A One-Dimensional Approach for Iris Identification

A One-Dimensional Approach for Iris Identification Yingzi Du a*, Robert Ives a, Delores Etter a, Thad Welch a, Chein-I Chang b a Electrical Engineering Department, United States Naval Academy, Annapolis,

### Iris Recognition based on Pupil using Canny edge detection and K- Means Algorithm Chinni. Jayachandra, H.Venkateswara Reddy

www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 2 Issue 1 Jan 2013 Page No. 221-225 Iris Recognition based on Pupil using Canny edge detection and K- Means

### Iris Pattern Segmentation using Automatic Segmentation and Window Technique

Iris Pattern Segmentation using Automatic Segmentation and Window Technique Swati Pandey 1 Department of Electronics and Communication University College of Engineering, Rajasthan Technical University,

### Software Development Kit to Verify Quality Iris Images

Software Development Kit to Verify Quality Iris Images Isaac Mateos, Gualberto Aguilar, Gina Gallegos Sección de Estudios de Posgrado e Investigación Culhuacan, Instituto Politécnico Nacional, México D.F.,

### Iris based Human Identification using Median and Gaussian Filter

Iris based Human Identification using Median and Gaussian Filter Geetanjali Sharma 1 and Neerav Mehan 2 International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(3), pp. 456-461

IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 2, Issue 2, Apr- Generating an Iris Code Using Iris Recognition for Biometric Application S.Banurekha 1, V.Manisha

### Image Enhancement using Histogram Equalization and Spatial Filtering

Image Enhancement using Histogram Equalization and Spatial Filtering Fari Muhammad Abubakar 1 1 Department of Electronics Engineering Tianjin University of Technology and Education (TUTE) Tianjin, P.R.

### Iris Recognition using Left and Right Iris Feature of the Human Eye for Bio-Metric Security System

Iris Recognition using Left and Right Iris Feature of the Eye for Bio-Metric Security System B. Thiyaneswaran Assistant Professor, ECE, Sona College of Technology Salem, Tamilnadu, India. S. Padma Professor,

### Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

### [Kalsi*, 4.(7): July, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785

IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY EFFICIENT BIOMETRIC IRIS RECOGNITION USING GAMMA CORRECTION & HISTOGRAM THRESHOLDING WITH PCA Jasvir Singh Kalsi*, Priyadarshani

### Tan-Hsu Tan Dept. of Electrical Engineering National Taipei University of Technology Taipei, Taiwan (ROC)

Munkhjargal Gochoo, Damdinsuren Bayanduuren, Uyangaa Khuchit, Galbadrakh Battur School of Information and Communications Technology, Mongolian University of Science and Technology Ulaanbaatar, Mongolia

### A Statistical Sampling Strategy for Iris Recognition

A Statistical Sampling Strategy for Iris Recognition Luis E. Garza Castanon^, Saul Monies de Oca^, and Ruben Morales-Menendez'- 1 Department of Mechatronics and Automation, ITESM Monterrey Campus, {legarza,

### Fast Subsequent Color Iris Matching in large Database

www.ijcsi.org 72 Fast Subsequent Color Iris Matching in large Database Adnan Alam Khan 1, Safeeullah Soomro 2 and Irfan Hyder 3 1 PAF-KIET Department of Telecommunications, Employer of Institute of Business

### Authenticated Automated Teller Machine Using Raspberry Pi

Authenticated Automated Teller Machine Using Raspberry Pi 1 P. Jegadeeshwari, 2 K.M. Haripriya, 3 P. Kalpana, 4 K. Santhini Department of Electronics and Communication, C K college of Engineering and Technology.

### A Novel Image Deblurring Method to Improve Iris Recognition Accuracy

A Novel Image Deblurring Method to Improve Iris Recognition Accuracy Jing Liu University of Science and Technology of China National Laboratory of Pattern Recognition, Institute of Automation, Chinese

### Global and Local Quality Measures for NIR Iris Video

Global and Local Quality Measures for NIR Iris Video Jinyu Zuo and Natalia A. Schmid Lane Department of Computer Science and Electrical Engineering West Virginia University, Morgantown, WV 26506 jzuo@mix.wvu.edu

### License Plate Localisation based on Morphological Operations

License Plate Localisation based on Morphological Operations Xiaojun Zhai, Faycal Benssali and Soodamani Ramalingam School of Engineering & Technology University of Hertfordshire, UH Hatfield, UK Abstract

### A New Fake Iris Detection Method

A New Fake Iris Detection Method Xiaofu He 1, Yue Lu 1, and Pengfei Shi 2 1 Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China {xfhe,ylu}@cs.ecnu.edu.cn

### An Adaptive Kernel-Growing Median Filter for High Noise Images. Jacob Laurel. Birmingham, AL, USA. Birmingham, AL, USA

An Adaptive Kernel-Growing Median Filter for High Noise Images Jacob Laurel Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA Electrical and Computer

### Automatic Iris Segmentation Using Active Near Infra Red Lighting

Automatic Iris Segmentation Using Active Near Infra Red Lighting Carlos H. Morimoto Thiago T. Santos Adriano S. Muniz Departamento de Ciência da Computação - IME/USP Rua do Matão, 1010, São Paulo, SP,

### A Novel Multi-diagonal Matrix Filter for Binary Image Denoising

Columbia International Publishing Journal of Advanced Electrical and Computer Engineering (2014) Vol. 1 No. 1 pp. 14-21 Research Article A Novel Multi-diagonal Matrix Filter for Binary Image Denoising

### Urban Feature Classification Technique from RGB Data using Sequential Methods

Urban Feature Classification Technique from RGB Data using Sequential Methods Hassan Elhifnawy Civil Engineering Department Military Technical College Cairo, Egypt Abstract- This research produces a fully

### Image Extraction using Image Mining Technique

IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 9 (September. 2013), V2 PP 36-42 Image Extraction using Image Mining Technique Prof. Samir Kumar Bandyopadhyay,

### Comparison between Open CV and MATLAB Performance in Real Time Applications MATLAB)

Anaz: Comparison between Open CV and MATLAB Performance in Real Time -- Comparison between Open CV and MATLAB Performance in Real Time Applications Ammar Sameer Anaz Diyaa Mehadi Faris ammar3303@gmail.com

### Adaptive Fingerprint Binarization by Frequency Domain Analysis

Adaptive Fingerprint Binarization by Frequency Domain Analysis Josef Ström Bartůněk, Mikael Nilsson, Jörgen Nordberg, Ingvar Claesson Department of Signal Processing, School of Engineering, Blekinge Institute

### Fast Inverse Halftoning

Fast Inverse Halftoning Zachi Karni, Daniel Freedman, Doron Shaked HP Laboratories HPL-2-52 Keyword(s): inverse halftoning Abstract: Printers use halftoning to render printed pages. This process is useful

### Feature Extraction Techniques for Dorsal Hand Vein Pattern

Feature Extraction Techniques for Dorsal Hand Vein Pattern Pooja Ramsoful, Maleika Heenaye-Mamode Khan Department of Computer Science and Engineering University of Mauritius Mauritius pooja.ramsoful@umail.uom.ac.mu,

### Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners

Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners Bozhao Tan and Stephanie Schuckers Department of Electrical and Computer Engineering, Clarkson University,

### A Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition and Mean Absolute Deviation

Sensors & Transducers, Vol. 6, Issue 2, December 203, pp. 53-58 Sensors & Transducers 203 by IFSA http://www.sensorsportal.com A Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition

### Eye Contact Camera System for VIDEO Conference

Eye Contact Camera System for VIDEO Conference Takuma Funahashi, Takayuki Fujiwara and Hiroyasu Koshimizu School of Information Science and Technology, Chukyo University e-mail: takuma@koshi-lab.sist.chukyo-u.ac.jp,

### IRIS RECOGNITION USING GABOR

IRIS RECOGNITION USING GABOR Shirke Swati D.. Prof.Gupta Deepak ME-COMPUTER-I Assistant Prof. ME COMPUTER CAYMT s Siddhant COE, CAYMT s Siddhant COE Sudumbare,Pune Sudumbare,Pune Abstract The iris recognition

### International Journal of Advanced Research in Computer Science and Software Engineering

Volume 3, Issue 4, April 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Novel Approach

### Learning Hierarchical Visual Codebook for Iris Liveness Detection

Learning Hierarchical Visual Codebook for Iris Liveness Detection Hui Zhang 1,2, Zhenan Sun 2, Tieniu Tan 2, Jianyu Wang 1,2 1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences 2.National

### AN EXPANDED-HAAR WAVELET TRANSFORM AND MORPHOLOGICAL DEAL BASED APPROACH FOR VEHICLE LICENSE PLATE LOCALIZATION IN INDIAN CONDITIONS

AN EXPANDED-HAAR WAVELET TRANSFORM AND MORPHOLOGICAL DEAL BASED APPROACH FOR VEHICLE LICENSE PLATE LOCALIZATION IN INDIAN CONDITIONS Mo. Avesh H. Chamadiya 1, Manoj D. Chaudhary 2, T. Venkata Ramana 3

### Critical Literature Survey on Iris Biometric Recognition

2017 IJSRST Volume 3 Issue 6 Print ISSN: 2395-6011 Online ISSN: 2395-602X Themed Section: Science and Technology Critical Literature Survey on Iris Biometric Recognition Shailesh Arrawatia 1, Priyanka

### Efficient Construction of SIFT Multi-Scale Image Pyramids for Embedded Robot Vision

Efficient Construction of SIFT Multi-Scale Image Pyramids for Embedded Robot Vision Peter Andreas Entschev and Hugo Vieira Neto Graduate School of Electrical Engineering and Applied Computer Science Federal

### Image Recognition for PCB Soldering Platform Controlled by Embedded Microchip Based on Hopfield Neural Network

436 JOURNAL OF COMPUTERS, VOL. 5, NO. 9, SEPTEMBER Image Recognition for PCB Soldering Platform Controlled by Embedded Microchip Based on Hopfield Neural Network Chung-Chi Wu Department of Electrical Engineering,

### Selection of parameters in iris recognition system

Multimed Tools Appl (2014) 68:193 208 DOI 10.1007/s11042-012-1035-y Selection of parameters in iris recognition system Tomasz Marciniak Adam Dabrowski Agata Chmielewska Agnieszka Anna Krzykowska Published

### INTERNATIONAL RESEARCH JOURNAL IN ADVANCED ENGINEERING AND TECHNOLOGY (IRJAET)

INTERNATIONAL RESEARCH JOURNAL IN ADVANCED ENGINEERING AND TECHNOLOGY (IRJAET) www.irjaet.com ISSN (PRINT) : 2454-4744 ISSN (ONLINE): 2454-4752 Vol. 1, Issue 4, pp.240-245, November, 2015 IRIS RECOGNITION

### Feature Extraction of Human Lip Prints

Journal of Current Computer Science and Technology Vol. 2 Issue 1 [2012] 01-08 Corresponding Author: Samir Kumar Bandyopadhyay, Department of Computer Science, Calcutta University, India. Email: skb1@vsnl.com

### Automatic Locating the Centromere on Human Chromosome Pictures

Automatic Locating the Centromere on Human Chromosome Pictures M. Moradi Electrical and Computer Engineering Department, Faculty of Engineering, University of Tehran, Tehran, Iran moradi@iranbme.net S.

### ScienceDirect. Improvement of the Measurement Accuracy and Speed of Pupil Dilation as an Indicator of Comprehension

Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 35 (2014 ) 1202 1209 18th International Conference in Knowledge Based and Intelligent Information and Engineering Systems

### Real Time Word to Picture Translation for Chinese Restaurant Menus

Real Time Word to Picture Translation for Chinese Restaurant Menus Michelle Jin, Ling Xiao Wang, Boyang Zhang Email: mzjin12, lx2wang, boyangz @stanford.edu EE268 Project Report, Spring 2014 Abstract--We

### Combined Approach for Face Detection, Eye Region Detection and Eye State Analysis- Extended Paper

International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 9 (September 2014), PP.57-68 Combined Approach for Face Detection, Eye

### Improving Far and FRR of an Iris Recognition System

IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 09 February 2017 ISSN (online): 2349-6010 Improving Far and FRR of an Iris Recognition System Neha Kochher Assistant

### 中国科技论文在线. An Efficient Method of License Plate Location in Natural-scene Image. Haiqi Huang 1, Ming Gu 2,Hongyang Chao 2

Fifth International Conference on Fuzzy Systems and Knowledge Discovery n Efficient ethod of License Plate Location in Natural-scene Image Haiqi Huang 1, ing Gu 2,Hongyang Chao 2 1 Department of Computer

### Performance evaluation of several adaptive speckle filters for SAR imaging. Markus Robertus de Leeuw 1 Luis Marcelo Tavares de Carvalho 2

Performance evaluation of several adaptive speckle filters for SAR imaging Markus Robertus de Leeuw 1 Luis Marcelo Tavares de Carvalho 2 1 Utrecht University UU Department Physical Geography Postbus 80125

### An Efficient Method for Vehicle License Plate Detection in Complex Scenes

Circuits and Systems, 011,, 30-35 doi:10.436/cs.011.4044 Published Online October 011 (http://.scirp.org/journal/cs) An Efficient Method for Vehicle License Plate Detection in Complex Scenes Abstract Mahmood

### Blur Estimation for Barcode Recognition in Out-of-Focus Images

Blur Estimation for Barcode Recognition in Out-of-Focus Images Duy Khuong Nguyen, The Duy Bui, and Thanh Ha Le Human Machine Interaction Laboratory University Engineering and Technology Vietnam National

### Segmentation of Fingerprint Images Using Linear Classifier

EURASIP Journal on Applied Signal Processing 24:4, 48 494 c 24 Hindawi Publishing Corporation Segmentation of Fingerprint Images Using Linear Classifier Xinjian Chen Intelligent Bioinformatics Systems

### Iris Recognition based on Local Mean Decomposition

Appl. Math. Inf. Sci. 8, No. 1L, 217-222 (2014) 217 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/081l27 Iris Recognition based on Local Mean Decomposition

### Noise Reduction Technique in Synthetic Aperture Radar Datasets using Adaptive and Laplacian Filters

RESEARCH ARTICLE OPEN ACCESS Noise Reduction Technique in Synthetic Aperture Radar Datasets using Adaptive and Laplacian Filters Sakshi Kukreti*, Amit Joshi*, Sudhir Kumar Chaturvedi* *(Department of Aerospace

### Vehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction

Vehicle License Plate Recognition System Using LoG Operator for Edge Detection and Radon Transform for Slant Correction Jaya Gupta, Prof. Supriya Agrawal Computer Engineering Department, SVKM s NMIMS University

### NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT:

IJCE January-June 2012, Volume 4, Number 1 pp. 59 67 NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT: A COMPARATIVE STUDY Prabhdeep Singh1 & A. K. Garg2

### ARRAY PROCESSING FOR INTERSECTING CIRCLE RETRIEVAL

16th European Signal Processing Conference (EUSIPCO 28), Lausanne, Switzerland, August 25-29, 28, copyright by EURASIP ARRAY PROCESSING FOR INTERSECTING CIRCLE RETRIEVAL Julien Marot and Salah Bourennane

### Edge Detection of Sickle Cells in Red Blood Cells

Edge Detection of Sickle Cells in Red Blood Cells Aruna N.S. *, Hariharan S. # * Research Scholar Electrical& Electronics Engineering Department, College of Engineering Trivandrum. University of Kerala.

### Implementation of License Plate Recognition System in ARM Cortex A8 Board

www..org 9 Implementation of License Plate Recognition System in ARM Cortex A8 Board S. Uma 1, M.Sharmila 2 1 Assistant Professor, 2 Research Scholar, Department of Electrical and Electronics Engg, College

### Control a 2-Axis Servomechanism by Gesture Recognition using a Generic WebCam

Tavares, J. M. R. S.; Ferreira, R. & Freitas, F. / Control a 2-Axis Servomechanism by Gesture Recognition using a Generic WebCam, pp. 039-040, International Journal of Advanced Robotic Systems, Volume

### Iris Recognition-based Security System with Canny Filter

Canny Filter Dr. Computer Engineering Department, University of Technology, Baghdad-Iraq E-mail: hjhh2007@yahoo.com Received: 8/9/2014 Accepted: 21/1/2015 Abstract Image identification plays a great role

### A new seal verification for Chinese color seal

Edith Cowan University Research Online ECU Publications 2011 2011 A new seal verification for Chinese color seal Zhihu Huang Jinsong Leng Edith Cowan University 10.4028/www.scientific.net/AMM.58-60.2558

### IDENTIFICATION OF FISSION GAS VOIDS. Ryan Collette

IDENTIFICATION OF FISSION GAS VOIDS Ryan Collette Introduction The Reduced Enrichment of Research and Test Reactor (RERTR) program aims to convert fuels from high to low enrichment in order to meet non-proliferation

### Contrast Enhancement for Fog Degraded Video Sequences Using BPDFHE

Contrast Enhancement for Fog Degraded Video Sequences Using BPDFHE C.Ramya, Dr.S.Subha Rani ECE Department,PSG College of Technology,Coimbatore, India. Abstract--- Under heavy fog condition the contrast

### Contrast adaptive binarization of low quality document images

Contrast adaptive binarization of low quality document images Meng-Ling Feng a) and Yap-Peng Tan b) School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

### Development of Image Processing Tools for Analysis of Laser Deposition Experiments

Development of Image Processing Tools for Analysis of Laser Deposition Experiments Todd Sparks Department of Mechanical and Aerospace Engineering University of Missouri, Rolla Abstract Microscopical metallography

### INDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION

International Journal of Computer Science and Communication Vol. 2, No. 2, July-December 2011, pp. 593-599 INDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION Chetan Sharma 1 and Amandeep Kaur 2 1

### Digital Image Processing 3/e

Laboratory Projects for Digital Image Processing 3/e by Gonzalez and Woods 2008 Prentice Hall Upper Saddle River, NJ 07458 USA www.imageprocessingplace.com The following sample laboratory projects are

### Retinal blood vessel extraction

Retinal blood vessel extraction Surya G 1, Pratheesh M Vincent 2, Shanida K 3 M. Tech Scholar, ECE, College, Thalassery, India 1,3 Assistant Professor, ECE, College, Thalassery, India 2 Abstract: Image

### Deep Green. System for real-time tracking and playing the board game Reversi. Final Project Submitted by: Nadav Erell

Deep Green System for real-time tracking and playing the board game Reversi Final Project Submitted by: Nadav Erell Introduction to Computational and Biological Vision Department of Computer Science, Ben-Gurion

### Pupil Segmentation of Abnormal Eye using Image Enhancement in Spatial Domain

IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Pupil Segmentation of Abnormal Eye using Image Enhancement in Spatial Domain To cite this article: R. A. Ramlee et al 2017 IOP

### Research on the Face Image Detection in Coal Mine Environment

2016 International Conference on Electronic Information Technology and Intellectualization (ICEITI 2016) ISBN: 978-1-60595-364-9 Research on the Face Image Detection in Coal Mine Environment Xiucai Guo

### A Generative Model for Fingerprint Minutiae

A Generative Model for Fingerprint Minutiae Qijun Zhao, Yi Zhang Sichuan University {qjzhao, yi.zhang}@scu.edu.cn Anil K. Jain Michigan State University jain@cse.msu.edu Nicholas G. Paulter Jr., Melissa

### Simulation of a mobile robot navigation system

Edith Cowan University Research Online ECU Publications 2011 2011 Simulation of a mobile robot navigation system Ahmed Khusheef Edith Cowan University Ganesh Kothapalli Edith Cowan University Majid Tolouei

### Method for Real Time Text Extraction of Digital Manga Comic

Method for Real Time Text Extraction of Digital Manga Comic Kohei Arai Information Science Department Saga University Saga, 840-0027, Japan Herman Tolle Software Engineering Department Brawijaya University

### Gaussian and Fast Fourier Transform for Automatic Retinal Optic Disc Detection

Gaussian and Fast Fourier Transform for Automatic Retinal Optic Disc Detection Arif Muntasa 1, Indah Agustien Siradjuddin 2, and Moch Kautsar Sophan 3 Informatics Department, University of Trunojoyo Madura,

### Impact of Resolution and Blur on Iris Identification

100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 Abstract

### Recursive Plateau Histogram Equalization for the Contrast Enhancement of the Infrared Images

2 3rd International Conference on Computer and Electrical Engineering ICCEE 2) IPCSIT vol. 53 22) 22) IACSIT Press, Singapore DOI:.7763/IPCSIT.22.V53.No..7 Recursive Plateau Histogram Equalization for

### Preprocessing of Digitalized Engineering Drawings

Modern Applied Science; Vol. 9, No. 13; 2015 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Preprocessing of Digitalized Engineering Drawings Matúš Gramblička 1 &

### Segmentation using Saturation Thresholding and its Application in Content-Based Retrieval of Images

Segmentation using Saturation Thresholding and its Application in Content-Based Retrieval of Images A. Vadivel 1, M. Mohan 1, Shamik Sural 2 and A.K.Majumdar 1 1 Department of Computer Science and Engineering,

### Automatic License Plate Recognition System using Histogram Graph Algorithm

Automatic License Plate Recognition System using Histogram Graph Algorithm Divyang Goswami 1, M.Tech Electronics & Communication Engineering Department Marudhar Engineering College, Raisar Bikaner, Rajasthan,

### SRI VENKATESWARA COLLEGE OF ENGINEERING. COURSE DELIVERY PLAN - THEORY Page 1 of 6

COURSE DELIVERY PLAN - THEORY Page 1 of 6 Department of Electronics and Communication Engineering B.E/B.Tech/M.E/M.Tech : EC Regulation: 2013 PG Specialisation : NA Sub. Code / Sub. Name : IT6005/DIGITAL