Commenced in January 2007
Paper Count: 31172
Illumination Invariant Face Recognition using Supervised and Unsupervised Learning Algorithms
Abstract:In this paper, a comparative study of application of supervised and unsupervised learning algorithms on illumination invariant face recognition has been carried out. The supervised learning has been carried out with the help of using a bi-layered artificial neural network having one input, two hidden and one output layer. The gradient descent with momentum and adaptive learning rate back propagation learning algorithm has been used to implement the supervised learning in a way that both the inputs and corresponding outputs are provided at the time of training the network, thus here is an inherent clustering and optimized learning of weights which provide us with efficient results.. The unsupervised learning has been implemented with the help of a modified Counterpropagation network. The Counterpropagation network involves the process of clustering followed by application of Outstar rule to obtain the recognized face. The face recognition system has been developed for recognizing faces which have varying illumination intensities, where the database images vary in lighting with respect to angle of illumination with horizontal and vertical planes. The supervised and unsupervised learning algorithms have been implemented and have been tested exhaustively, with and without application of histogram equalization to get efficient results.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078015Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1354
 Kuang-Chih Lee, Jeffrey Ho, and David J. Kriegman, "Acquiring Linear Subspaces for Face Recognition under Variable Lighting", IEEE Transactions on pattern analysis and machine intelligence, Vol.27, No. 5, pp. 684-698, 2005.
 Volker Blanz, Thomas Vetter, "Face Recognition Based on Fitting a 3D Morphable Model", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.9, pp.1-12, 2003.
 George Bebis, Aglika Gyaourova, Saurabh Singh, Ioannis Pavlidis," Face recognition by fusing thermal infrared and visible imagery", Image and Vision Computing, 24,pp. 727-742, 2006.
 Kevin Curran, Xuelong Li, Neil Mc Caughley, "The Use of Neural Networks in Real-time Face Detection", Journal of Computer Sciences, Vol.1, No.1, pp.47-62, 2005.
 Henry A. Rowley, Shumeet Baluja, Takeo Kanade, "Neural Network- Based Face Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998.
 Abhijit Mukherjee , Pravin R. Raijade , R.I.K. Moorthy , A. Kakodkar, "Artificial neural networks in CT-PT contact detection in a PHWR", Nuclear Engineering and Design, pp.303-309, 1998.
 Markandey Singh, Ritwaj Ratan, Sarvesh Kumar, "Optical Character Recognition for printed Tamil text using Unicode", Journal of Zhejiang University Science, Vol.6A, No.11, pp.1297-1305, 2005.
 Christopher Gan, Visit Limsombunchai, Mike Clemes and Amy Weng," Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models", Journal of Social Sciences, Vol.1, No.4, pp.211-219, 2005.
 B Karthikeyan, S Gopal, S. Venkatesh, "A heuristic complex probabilistic neural network system for partial discharge pattern classification", Journal of Indian Institute of Science, 85, pp. 279-294, 2005.
 Dipti Deodhare, NNR Ranga Suri, R. Amit, "Preprocessing and Image Enhancement Algorithms for a Form-based Intelligent Character Recognition System", International journal of computer science and applications, Vol.2, No.2, pp.131-144, 2005.
 Dinesh Kumar, C.S. Rai, and Shakti Kumar, "An Experimental Comparison of Unsupervised Learning Techniques for Face Recognition", International journal of computer and information science and engineering, Vol.1, No.3, pp.158-166, 2007.
 Peter J. Roberts, Rodney A. Walker, "Application of a Counter Propagation Neural Network for Star Identification", American Institute of Aeronautics and Astronautics, 2005.
 Jayendra Krishna, Laxmi Srivastava, "Counterpropagation Neural Network for Solving Power Flow Problem", International Journal of Intelligent Technology, Vol.1, No.1, pp.57-62, 2005.
 Muhammad Faisal Zafar, Dzulkifli Mohamad, and Razib M. Othman, "On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net", Proceedings of World Academy of Science, Engineering and Technology, Vol.10, pp.232-237, 2005.
 Arpad Barsia, Christian Heipkeb, "Artificial Neural Networks For The Detection Of Road Junctions In Aerial Images", ISPRS Archives, Vol.34, No.3, pp.113-118, 2003.
 Alioune Ngom, Ivan Stojmenovic', Veljko Milutinovic', "STRIPÔÇöA Strip-Based Neural-Network Growth Algorithm for Learning Multiple- Valued Functions", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, No.2, pp.212-227, 2001.
 Enrique Castillo,Avda de Los Castros,Bertha Guijarro-Berdi,Oscar Fontenla-Romero,Amparo Alonso-Betanzos, "A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis", Journal of Machine Learning Research, Vol.7, pp.1159-1182, 2006.
 Edward Wilson," Back propagation Learning for Systems with Discrete-Valued Functions", Proceedings of the World Congress on Neural Networks, 1994.
 Wan-Young Chung, Seung-Chul Lee, "An Air Quality Sensor System with a Momentum Back Propagation Neural Network", Journal of the Korean Physical Society, Vol. 49, No. 3, pp. 1087-1091, 2006.