Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/688
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dc.contributor.supervisorSingla, Sunil Kumar-
dc.contributor.authorGupta, Sourabh-
dc.date.accessioned2008-09-24T10:26:24Z-
dc.date.available2008-09-24T10:26:24Z-
dc.date.issued2008-09-24T10:26:24Z-
dc.identifier.urihttp://hdl.handle.net/10266/688-
dc.descriptionM.E. (Electronic Instrumentation and Control Engineering)en
dc.description.abstractBiometric deals with identifying individual with the help of their biological data. Human face recognition is a potential method of biometric authentication. Face is a primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. The human ability to recognize faces is remarkable. People can recognize thousands of faces learned throughout their lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards or changes in hair style etc. Face recognition is used in many applications such as security systems, credit card verification and criminal identification. Due to numerous potential applications face recognition has become a very active research area. Face recognition is an interdisciplinary research area, involving researchers from pattern recognition, computer vision, and graphics, image processing/ understanding, statistical computing and machine learning. In the present work a neural network based face recognition system has been developed. In the developed system the Gabor filter bank is used to overcome the problem of rotation. The system is commenced on convolving a face image after preprocessing the image at different scales and orientations. The neural network is used as a classifier in which the weights of the neurons are updated by supervised learning (target are set as .9 and .1) using Resilient backpropagation algorithm. Yale database has been used for training the network and for testing the authenticity of the person. If the person belongs to the trained database then the network will return the value 0.9 otherwise 0.1. With the developed system an accuracy of 90% has been achieved.en
dc.format.extent119808 bytes-
dc.format.mimetypeapplication/msword-
dc.language.isoen_USen
dc.subjectFace recognitionen
dc.subjectNeural Networken
dc.subjectConvolutionen
dc.titleFace Recognition System Using Neural Networken
dc.typeThesisen
Appears in Collections:Masters Theses@EIED

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