Illumination Invariant Face Recognition using Supervised and Unsupervised Learning Algorithms
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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] Henry A. Rowley, Shumeet Baluja, Takeo Kanade, "Neural Network-
Based Face Detection", IEEE Transactions on Pattern Analysis and
Machine Intelligence, 1998.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] Peter J. Roberts, Rodney A. Walker, "Application of a Counter
Propagation Neural Network for Star Identification", American
Institute of Aeronautics and Astronautics, 2005.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] Edward Wilson," Back propagation Learning for Systems with
Discrete-Valued Functions", Proceedings of the World Congress on
Neural Networks, 1994.
[19] 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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] Henry A. Rowley, Shumeet Baluja, Takeo Kanade, "Neural Network-
Based Face Detection", IEEE Transactions on Pattern Analysis and
Machine Intelligence, 1998.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] Peter J. Roberts, Rodney A. Walker, "Application of a Counter
Propagation Neural Network for Star Identification", American
Institute of Aeronautics and Astronautics, 2005.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] Edward Wilson," Back propagation Learning for Systems with
Discrete-Valued Functions", Proceedings of the World Congress on
Neural Networks, 1994.
[19] 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.
@article{"International Journal of Information, Control and Computer Sciences:60864", author = "Shashank N. Mathur and Anil K. Ahlawat and Virendra P. Vishwakarma", title = "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.", keywords = "Artificial Neural Networks, back propagation,
Counterpropagation networks, face recognition, learning algorithms.", volume = "2", number = "7", pages = "2497-6", }
