Recognition of an individual using the unique features of human face

Face recognition has been gaining popularity for long time in various fields of human computer interaction. Moreover face recognition technique is widely used for automatic biometric security control, document verification, criminal investigation etc. In this paper we propose a new approach of using PCA based face recognition method for human verification. PCA based method seems to be interested due to its simplicity and better accuracy. In our proposed method two stages of authentication are performed for recognizing a single candidate individual. At first stage the candidate face is matched with all stored faces and only few best matched samples are isolated to use as second stage training samples. Here in both stages PCA is used for extracting significant features of face. Our proposed approach showed 1.5% better accuracy for ORL face database and 0.52% better accuracy for Face94 database.

[1]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[3]  Shyla Afroge,et al.  PCA and back-propagation neural network based face recognition system , 2015, 2015 18th International Conference on Computer and Information Technology (ICCIT).

[4]  Fang Fang.  Survey of human face detection and recognition technology , 2006 .

[5]  Daoqiang Zhang,et al.  Diagonal principal component analysis for face recognition , 2006, Pattern Recognit..

[6]  S. T. Zuhori,et al.  Face recognition using Principle Component Analysis and Linear Discriminant Analysis , 2015, 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[7]  Chengjun Liu,et al.  Independent component analysis of Gabor features for face recognition , 2003, IEEE Trans. Neural Networks.

[8]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[9]  B. Lovell,et al.  Robust Face Recognition in Rotated Eigenspaces , 2007 .

[10]  N. Jamil,et al.  Face recognition using neural networks , 2001, Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century..

[11]  Rongchun Zhao,et al.  Face Recognition Using Multi-feature and Radial Basis Function Network , 2003, VIP.

[12]  Department of Electrical,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.

[13]  Firoz Mahmud,et al.  Human face recognition using PCA based Genetic Algorithm , 2014, 2014 International Conference on Electrical Engineering and Information & Communication Technology.

[14]  B. Lovell,et al.  Robust Face Recognition in Rotated Eigen Space , 2007 .

[15]  Nadeem. Vawda.  Face recognition with Eigenfaces : a detailed study. , 2012 .

[16]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[17]  Y. L. Yu,et al.  Face recognition with eigenfaces , 1994, Proceedings of 1994 IEEE International Conference on Industrial Technology - ICIT '94.

[18]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.