Door Lock System through Face Recognition Using MATLAB

Ayushi Gupta¹ , Ekta Sharma² , Neha Sachan³ , Neha Tiwari⁴

Section:Review Paper, Product Type: Isroset-Journal
Vol.1 , Issue.3 , pp.51-55, May-2013

Online published on Jul 07, 2013

Copyright © Ayushi Gupta, Ekta Sharma, Neha Sachan , Neha Tiwari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Face recognition is a biometric technology with a wide range of potential applications such as access control, banking, information security, human computer interaction, virtual reality, database retrieval etc. This paper addresses the building of face recognition system by using Principal Component Analysis (PCA) method. PCA is a statistical approach used for reducing the number of variables in face recognition. While extracting the most relevant information (feature) contained in the images (face). In PCA, every image in the training set can be represented as a linear combination of weighted eigenvectors called as “Eigenfaces”. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a new image (test image) onto the subspace spanned by the eigenfaces and then classification is done by distance measure methods such as Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system.

Key-Words / Index Term :
PCA, MAX232, MATLab

References :
[1] Principal Component Analysis for Face Recognition, by Saurabh P.Bahurupi, D.S.Chaudhari, ISSN: 2249 – 8958, Volume-1, Issue-5, June 2012
[2] Automatic face recognition using principal Component Analysis with DCT by Miss.Renke Pradnya Sunil (Electronics and Telecommunication department, ,Rajarambapu Institute of Technology, India).
[3] http://www.face-rec.org/algorithms.
[4] Face Recognition edited by Kresimir Delac and Mislav Grgic,june 2007 edition.
[5] Face Recognition using Principle Component Analysis, Kyungnam Kim (Department of Computer Science University of Maryland, College Park MD 20742, USA )
[6] Sukhvinder Singh, Meenakshi Sharma and Dr. N Suresh Rao, “ Accurate Face Recognition Using PCA and LDA”, International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2011) Bangkok Dec., 2011.
[7] Hussein Rady, “Face Recognition using Principle Component Analysis with Different Distance Classifiers”(El-Shorouk Academy, Higher Institute for Computer & Information Technology, Egypt), IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.10, October 2011