Open Access Open Access  Restricted Access Subscription or Fee Access

An Efficient Method for Face Recognition System in Various Assorted Conditions

V. Karthikeyan, K. Vijayalakshmi, P. Jeyakumar

Abstract


In the beginning stage, face verification is done using easy method of geometric algorithm models, but the verification route has now developed into a scientific progress of complicated geometric representation and identical procedure. In recent years the technologies have boosted face recognition system into the healthy focus. Researcher‟s currently undergoing strong research on finding face recognition system for wider area information taken under hysterical elucidation dissimilarity. The proposed face recognition system consists of a narrative exposition-indiscreet preprocessing method, a hybrid Fourier-based facial feature extraction and a score fusion scheme. We have verified the face recognition in different lightening conditions (day or night) and at different locations (indoor or outdoor). Preprocessing, Image detection, Feature- extraction and Face recognition are the methods used for face verification system. This paper focuses mainly on the issue of toughness to lighting variations. The proposed system has obtained an average of 88.1% verification rate on Two-Dimensional images under different lightening conditions.

Keywords


Face Recognition, Score Fusion, Preprocessing Chain, Feature Extraction

Full Text:

PDF

References


P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss,The FERET evaluation methodology for face recognition algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1090–1104, Oct. 2000.

Average Half Face Recognition By Elastic Bunch Graph Matching Based On Distance Measurement “International Journal for Science and Emerging Technologies with Latest Trends” 3(1): 24-35 (2012)

P. Phillips, P. Grother, R. Micheals, D. Blackburn, E. Tabassi, and M. Bone, “Face recognition vendor test 2002: evaluation report,” 2003 [Online]. Available: http://www.frvt.org/

G. Deng and L.W. Cahill, (1994) ,on „An adaptive Gaussian filter for noise reduction and edge detection‟,‖ in Proc. IEEE Nucl. Sci. Symp. Med. Im. Conf., 1994, pp. 1615–1619.

K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, and A. Kostin et al., “Face authentication test on the BANCA database,” in Proc. Int. Conf. Pattern Recognit., Aug. 2004, vol. 4, pp. 523–532.

Hu, M. K., (1962) on „Visual Pattern Recognition by Moment Invariant‟, IRE Transaction on Information Theory, vol IT- 8, pp. 179-187.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. Bowyer, J. ChangK. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in Proc. IEEE. Comput. Vis. Pattern Recognit., Jun.2005, vol. 1, pp. 947–954.

Level set based Volumetric Anisotropic Diffusion for3D Image Filtering Chandrajit L. BajajDepartment of Computer Science and Institute of Computational andEngineering Sciences University of Texas, Austin, TX 78712

Kim, G. M., (1997), on „The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform‟, Journal of Control, Automation and System Engineering, vol. 3, no.5, pp. 511-519, 1997. 75

R. Ramamoorthi and P. Hanrahan, “On the relationship between radiance and irradiance: Determining the illumination of a convex Lambertian object,” J. Opt. Soc. Amer., vol. 18, no. 10, pp. 2448–2459, 2001.

K-means clustering via Principal component Analysis Appearing in Proceedings Copyright 2004 by the authors. of the 21st International Confer-ence on Machine Learning, Banff, Canada, 2004.

Face Recognition System Based on Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) Canadian Journal on Image Processing and Computer Vision Vol. 2, No. 4, April 2011

In Intelligent Biometric Techniques in Fingerprint and Face Recognition,eds. L.C. Jain et al., publ. CRC Press, ISBN 0-8493-2055-0, Chapter 11, pp. 355-396, (1999).


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.