Abstract
Recognition of object under uncontrolled illumination environment is imprecise and vague. A simple image preprocessing chain is taken for precept. Local binary pattern (LBP) is capable of reducing noise levels in background regions. Local ternary patterns (LTP) fragmenting less under noise in uniform regions. Gabor filter acts as a resounding filtering source for local spatial frequencies. Phase congruency is to extract the image in phase as well as in magnitude levels. The result is obtained by the KLDA based classifiers with combination of LBP and Gabor features. The above explained features are obtained from both the input and the data base image. In that the LBP and Gabor features are fused and the distance is calculated. If both the input and database images are same, the face is recognized; otherwise the face is not recognized. The simulation results exemplify the proposed technique for image with different lighting, expressions and structural defects.
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References
Tan, X., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. IEEE Transactions on Image Processing 19(6) (June 2010)
Kovesi, P.: Phase Congruen cy Detects Corners and Edges. School of ComputerScience & Software Engineering The University of Western Australia Crawley, W.A. 6009 pk@csse.uwa.edu.au
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM computing surveys 35(4), 399–458 (2003)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7) (July 1997)
Kernel principal component analysis, http://en.wikipedia.org/wiki/ Kernel analysis from Wikipedia, the free encyclopedia
Malik, J., Sainarayanan, G., Dahiya, R.: Corner Detection using Phase Congruency Features. In: International Conference on Signal and Image Processing (ICSIP), December 15-17 (2010)
Smith, A.R.: Gamma Correction. Technical Memo (September 9, 1995)
Gondane, S.G.: Recognition and Detection System in Human Face Under Variable Illumination Conditions. In: Third National Conference on Recent Trends in Computation and Signal Held on Amrita School of Engineering, March 1 (2011)
Gupta, R,. Mittal, H.P.A.: Robust Order-based Methods forFeature Description, Department of Computer Science and Engg. Indian Institute of Technology Madras, Chennai INDIA-600036
Bangalore, N., Young, R., Birch, P., Chatwin, C.: TrackingMoving Objects Using Band pass Filter Enhanced Localization and Automated Initialization of Active Contour Snakes Industrial Informatics Research group. In: Department of Engineering and Design, University Of Sussex, Brighton
Ding, W., Li, X., Wang, W.: Robust Image Corner Detection Using Local Line Detector and Phase Congruency Model. In: WASE International conference on Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China Information Engineering (ICIE), (2010)
Ahmadian, A., Mostafa, A.: An efficient texture classification algorithm using Gabor wavelet. Dept. of Biomedica System & Medical Phys., Tehran Univ. of Medical Sci., Iran Engineering in Medicine and Biology Society 1 (September 17-21, 2003); (Date of Current Version: April 5, 2004)
Zhang, C., Guo, K., Yu, G.: An Analysis of Gabor Wavelet Algorithm for Tracking Driver’s Feature Point. In: International Conference Key Lab. of Intell. Transp. Syst. Technol., Nat. Center of ITS Eng. & Technol. Electrical and Control Engineering (ICECE), Beijing, China (November 2010)
Ekenel, H.K., Fischer, M., Tekeli, E., Stiefelhagen, R.: A Local Binary Pattern Domain Local Appearance Face Recognition. In: Signal Processing, Communication and Applications Conference (2008)
Verschae, R., Ruiz-del-Solar, J., Correa, M.: Face Recognitionin on Unconstrained Environments:A Comparative Study, Department of Electrical Engineering,Universidad de Chile
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Gondane, S.G., Dhivya, M., Shyam, D. (2011). Classifier Feature Extraction Techniques for Face Recognition System under Variable Illumination Conditions. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_11
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DOI: https://doi.org/10.1007/978-3-642-22714-1_11
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