This paper research work proposes automatic number plate and face recognition system. this research work first focusing for implementing a automatic number plate system based on optical character recognition and then face recognition system based on Principle Component Analysis(PCA) to standardize the faces illumination reducing in such way the variations for further features extraction; after developing both the system . In order to increase the security level this work proposes a automatic number plate and face recognition and verification system. The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e. g. Parliament, Supreme Court and also in residential apartments, toll collections booths etc to avoid the security check at entrance of the gates. The developed system first captures the vehicle image and vehicles authorize persons such as owner or registered person's faces. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition the resulting data is then used to compare with the records on a database. Once the number plate of authorize vehicle is identified and then faces of the persons verified by using face recognition system if face is also identified then vehicle can be allowed for restricted area otherwise not allowed. This work can be implemented and simulated in Matlab, and it performance is tested on real image and also tested on data base images. It is observed from the experimental results t that the developed system successfully detects and recognize the vehicle number plate images and faces of the authorize persons only.

1. Wright, J. and Yi Ma and Mairal, J. and Sapiro, G. and Huang, T. S. and Shuicheng Yan , Robust Face Recognition via Sparse Representation," IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009. pp. 597 -604, 2009
2. Fazl-Ersi, E. ; Tsotsos, J. K. ; , Local feature analysis for robust face recognition," IEEE Symposium on Computational Intelligence for Security and Defense Applications , page. 1-6 July 2009
3. Delac K. , Grgic M. , Grgic S. , Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set, International Journal of Imaging Systems and Technology, Vol. 15, Issue 5, 2006, pp. 252-260
4. D. Bryliuk and V. Starovoitov, Access Control by Face Recognition using Neural Networks and Negative Examples, 2nd International Conference on Arti cial Intelligence, pp. 428-436, Sept 2002.
5. Wright, J. and Yi Ma and Mairal, J. and Sapiro, G. and Huang, T. S. and Shuicheng Yan Sparse Representation for Computer Vision and Pattern Recognition" 2010 Proceedings of the IEEE, volume 98 pp 1031 -1044
6. Massimo Tistarelli, Manuele Bicego, Enrico Grosso, Dynamic face recognition: From human to machine vision, Image and Vision Computing, Volume 27, Issue 3, Special Issue on Multimodal Biometrics - Multimodal Biometrics Special Issue, Pages 222-232, February 2009
7. Kar, S. and Hiremath, S. and Joshi, D. G. and Chadda, V. K. and Bajpai, A. A Multi-Algorithmic Face Recognition System", International Conference on Advanced Computing and Communica-tions, 2006. ADCOM 2006. pp 321 -326, 2006
8. Nicolas Morizet, Frdric Amiel, Insaf Dris Hamed,Thomas Ea A Comparative Implementation of PCA Face Recognition Algorithm ,ICECS'07
9. TheExtendedYale Face Database B,http://vision. ucsd. edu/
10. Description page for the Yale Face Database B,

Anpr (automatic Number Plate Recognition) Principle Component Analysis (pca) Face Recognition Vehicle Number Plate Image Segmentation Matlab.