FACE RECOGNITION SYSTEM FOR MACHINE READABLE TRAVEL DOCUMENTS
DOI:
https://doi.org/10.47839/ijc.11.1.548Keywords:
Face detection, face recognition, eigenface basis ranking measure, gender determination.Abstract
This paper presents an upright frontal face recognition system, aimed to recognize faces on machine readable travel documents (MRTD). The system is able to handle large image databases with high processing speed and low detection and identification errors. In order to achieve high accuracy eyes are detected in the most probable regions, which narrows search area and therefore reduces computation time. Recognition is performed with the use of eigenface approach. The paper introduces eigenface basis ranking measure, which is helpful in challenging task of creating the basis for recognition purposes. To speed up identification process we split the database into males and females using high - performance AdaBoost classifier. At the end of the paper the results of the tests in speed and accuracy are given.References
M. Yang, Recent advances in face detection, Tutorial, IEEE International Conference on Pattern Recognition, Cambridge, 2004.
E. Osuna, R. Freund and F. Girosi, Support Vector Machines: Training and Applications, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 1997.
S. Romdhani, P. Torr, B. Scholkopf and A. Blake, Computationally efficient face detection, In Proceeding of the 8th International Conference on Computer Vision, vol. 2, Vancouver, 2001, pp. 695-700.
H. Schneiderman, A Statistical Approach to 3D Object Detection Applied to Faces and Cars, Dissertation Thesis at Carnegie Mellon University, Pittsburgh, 2000.
H. Rowley, S. Baluja and T. Kanade, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, (20) 1 (1998), pp. 22-38.
P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision, (2004), pp. 137-154.
Y. Freund and R. Shapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, (1996) pp. 119-139.
M. Turk, and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, (3) 2 (1991), pp. 71-86.
S. Baluja, M. Sahami, and H. Rowley, Efficient face orientation discrimination, International Conference on Image Processing, 2004.
B. Moghaddam and M. Yang, Learning gender with support faces, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), (24) 5 (May 2002).
G. Shakhnarovich, P. Viola and B. Moghaddam, A unified learning framework for real time face detection and classification, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002
S. Baluja and H. Rowley, Boosting sex identification performance, International Journal of Computer Vision, (71) 1 (2007).
International Civil Aviation Organization. Machine Readable Travel Documents, Doc 9303, 2006.
R. Lienhart and J. Maydt, An extended set of Haar-like features for rapid object detection, In Proceedings of The IEEE International Conference on Image Processing, pp. 900-903.
P.J. Phillips, H. Moon, S.A. Rizvi and P.J. Rauss, The FERET evaluation methodology for face recognition algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, (22) (2000), pp. 1090-1104.
P.J. Phillips, H. Wechsler, J. Huang and P. Rauss, The FERET database and evaluation procedure for face recognition algorithms, Image and Vision Computing J., (16) 5 (1998), pp. 295-306.
S. Tulyakov and R. Sadykhov, Face recognition on machine readable travel documents: algorithms and results, In Proceedings of International Conference on Pattern Recognition and Information Processing, 2011.
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