Wednesday 24th of April 2024
 

A Neuro-Genetic System for Face Recognition


V. Sai Shanmuga Raja and Dr. S.P. Rajagopalan

Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as security systems, medical systems, entertainment, etc. A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. Face recognition is the preferred mode of identification by humans: it is natural, robust and non-intrusive. The proposed approach uses Neural and Genetic algorithm that overcomes low recognition rate, low accuracy and increased time of recovery. Optimization of neural network parameters is done using Genetic algorithm. This increases recognition accuracy and reduced training time.

Keywords: Genetic algorithm, Neural Network, Face recognition.

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ABOUT THE AUTHORS

V. Sai Shanmuga Raja
V. Saishanmuga raja is a Research Scholar at Dr. MGR Educational & Research Institute University in the Department of Computer Science and Engineering, Chennai, Tamilnadu. He is doing his research under the guidance of Dr. S.P. Rajagopalan. He received his B.E., degree in 2006 and ME degree in 2008 in Computer Science and Engineering from Anna University Chennai. He is presently working as Assistant Professor at Dr. MGR Educational & Research Institute University Chennai. His research interests are Data mining, Neural Networks, and Genetic Algorithm. He is a member of IEEE.

Dr. S.P. Rajagopalan
Dr.S.P. Rajagopalan received his M.Sc from IIT Madrs, M.Phil and Ph.D from Madras University, Chennai, India. He is presently working as a Professor (Emeritus) in Department of Computer applications in Dr. M.G.R. Educational and Research Institute, Chennai. He has 40 years of experience in Teaching and Research. He has published 4 Books. He has about 100 publications in International Journals and National Journals. His special fields of interest include Quantitative Techniques, Data Processing and Project Management, Management Information System, Programming Languages, Simulation, Text generation, Cryptography and Data Mining.


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