Paper Title : Human Iris Using Neural Network

ISSN : 2394-2231
Year of Publication : 2020

10.29126/23942231/IJCT-v7i2p9

Authors: Dr.E.Punarselvam, Mr.S.Gopi ,M.Hariharan, T.Hariharan, M.Karthi. , J.Vimalaadhithyan

         



MLA Style: Dr.E.Punarselvam, Mr.S.Gopi ,M.Hariharan, T.Hariharan, M.Karthi. , J.Vimalaadhithyan, "Human Iris Using Neural Network" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Dr.E.Punarselvam, Mr.S.Gopi ,M.Hariharan, T.Hariharan, M.Karthi. , J.Vimalaadhithyan, "Human Iris Using Neural Network" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Multi-biometric systems are being increasingly deployed in many large-scale biometric applications because they have several advantages such as lower error rates and larger population coverage compared to uni-biometric systems. However, multi-biometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. Traditional iris segmentation methods provide good results good result when iris images are recorded ideal imaging conditions. However the segmentation accuracy of an iris recognitions system considerably influences its performance especially in the case of non ideal iris images, iris datasets are collected from online for the further processes which are recorded in the visible and infrared imaging conditions are used, then fusion of an expanding and a shrinking active contour is developed for the iris segmentation by integration of a new pressure force on the active contour model. That is Active Contour Force (ACF) model is used for segmentation .To isolate the boundaries of an iris. Un circle normalization schema is employed to get normalized image from the segmented image.

Reference
[1] DeepIrisNet Deep Iris Representation with Applications in Iris Recognition and Cross Sensor Iris Recognition AbhikGangwar, Akanksha Joshi.2018. [2] E.Punarselvam,“Privacy and Secured Multiparty Data Categorization using Cloud Resources”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN(Online) : 2319 – 8753,ISSN (Print) : 2347-6710 Vol. 4, Special Issue 6,May 2015,pp 336-343. [3] Fusion of Iris and Periocular Biometricsfor Cross-Sensor Identification Author: Lihu Xiao, Zhenan Sun, and Tieniu TanAs a reliable personal identification 2016 [4] FIRME: Face and Iris Recognition for Mobile Engagement Author: Maria De Marsico Chiara Galdi Michele Nappi Daniel Riccioc 2014. [5]Ordinal Measures for Iris and cross-sensor iris recognitionAuthorGaldi Michele, and Tieniu Tan, Fellow,2014. [6] Ordinal Measures for Iris Recognition Author: Zhenan Sun, Member, and Tieniu Tan, Fellow, 2012. [7] Dr.E.Punarselvam,“Supervised and Semi Supervised Machine Learning Clustering Algorithm based on feature selection”, International Journal on Applications in Information and Communication Engineering, Volume 5 : Issue 2: November 2019, PP 19 – 24, ISSN (Online) : 2394 – 6237

Keywords
Language Integrated Query, Convolutional Neural Networks, Common Type System