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Independent Component Analysis of Edge Information for Face Recognition
Kailash Jagannath Karande, Sanjay N Talbar
Pages - 120 - 130     |    Revised - 05-08-2009     |    Published - 01-09-2009
Volume - 3   Issue - 3    |    Publication Date - June 2009  Table of Contents
Principle Component analysis (PCA),, Independent Component Analysis (ICA), Laplacian of Gaussian ( LoG, Canny edge detection, Euclidean distance classifier, Mahalanobis distance classifier.
In this paper we address the problem of face recognition using edge information as independent components. The edge information is obtained by using Laplacian of Gaussian (LoG) and Canny edge detection methods then preprocessing is done by using Principle Component analysis (PCA) before applying the Independent Component Analysis (ICA) algorithm for training of images. The independent components obtained by ICA algorithm are used as feature vectors for classification. The Euclidean distance and Mahalanobis distance classifiers are used for testing of images. The algorithm is tested on two different databases of face images for variation in illumination and facial poses up to 180 degree rotation angle.
CITED BY (40)  
1 Ouyang Ning, Zhonghuan Hong, Yuan Hua, Mo & Jianwen. (2015). Face Recognition Algorithm for illumination change. Electronic Technology, 41 (5), 152-155.
2 Gubbi, A., Azeem, M. F., & Nayakwadi, N. Z. H. (2015). Conventional Entropy Quantifier and Modified Entropy Quantifiers for Face Recognition. Procedia Computer Science, 46, 1529-1536.
3 Monteiro, J. C., & Cardoso, J. S. (2015). A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios. Sensors, 15(1), 1903-1924.
4 Dadi, H. S., & Mohan, P. K. Performance Evaluation of Eigen faces and Fisher faces with different pre-processed data sets.
5 Zhong Wen Jin. (2014). Research on face recognition method of using a linear subspace. Department of Computer Science, National Taipei University of Education Thesis, 1-73.
6 Wang Ke, Xiaopeng Feng, FENG Xue-zhi, Wu Guiping, & Li. (2014). Based on a two-dimensional edge detection omnidirectional Log Butterworth filter frequency domain methods. Surveying and Mapping, 42 (5), 682-690.
7 Linge, G. V., & Pawar, M. M. (2014). Face Recognition using Neural Network & Principal Component Analysis.
8 Mahmood, Z., Ali, T., Khattak, S., & Khan, S. U. (2014, December). A comparative study of baseline algorithms of face recognition. In Frontiers of Information Technology (FIT), 2014 12th International Conference on (pp. 263-268). IEEE.
9 Linge, G., & Pawar, M. Neural Network Based Face Recognition Using PCA.
10 Karimi, M. H., & Asemani, D. (2014). Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation. ISA transactions, 53(3), 834-844.
11 Wang Ke, Xiaopeng Feng, FENG Xue-zhi, Wu Guiping, & Li. (2013). LogButterworth two-dimensional edge detection filter omnidirectional frequency domain methods. Surveying and Mapping, 42 (5), 682-690.
12 Zhao, K. X., Peng, M. F., Tan, H., He, S. D., Shen, M. E., & He, J. B. (2013, November). Fault Diagnosis of Grounding Grid Based on Principal Component Analysis and Fuzzy Clustering. In Advanced Materials Research (Vol. 787, pp. 881-885).
13 Zeng, H., & Luo, R. (2013). Preferred Skin Color Enhancement of Digital Photographic Images. International Journal of Image Processing (IJIP), 7(4), 314.
14 Bhagat, V., & Atkare, R. R. (2013). Feature Based Face Recognition and Detection. International Journal Of Computer Science And Applications, 6(2).
15 Dabhade, S. B., Kazi, M. M., Rode, Y. S., Manza, R. R., & Kale, K. V. (2013, April). Face Recognition using Principle Component Analysis and Linear Discriminant Analysis: Comparative Study. In 2nd National Conference on Advacements in the Era of Multi-Disciplinary Systems AEMDS-2013, April (pp. 6-7).
16 Karande, K. J. (2013, April). Localized spatiotemporal modular ICA for face recognition. In Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on (pp. 66-70). IEEE.
17 Mittal, H. S., & Kaur, H. (2013). Face recognition using PCA and neural network. Int. J. Emerg. Sci. Eng, 1, 71-75.
18 Ebied, H. M., Revett, K., & Tolba, M. F. (2013). Evaluation of unsupervised feature extraction neural networks for face recognition. Neural Computing and Applications, 22(6), 1211-1222.
19 Chaudhary, U., Mubarak, C. M., Rehman, A., Riyaz, A., & Mazhar, S. (2012). Face recognition using PCA-BPNN algorithm. Int. J. Modren Eng. Res.(IJMER), 2, 1366-1370.
20 Wang Ke, Xiaopeng Feng, FENG Xue-zhi, Wu Guiping, & Li. (2012). Based on Improved Image Edge Detection dimensional discrete Hilbert transform. Surveying and Mapping, 41 (3), 0-416.
21 George, J. P. (2012). Development of efficient biometric recognition algorithms based on fingerprint and face (Doctoral dissertation, Christ University, Bangalore).
22 Ravi, J., Tevaramani, S. S., & Raja, K. B. (2012, February). Face recognition using DT-CWT and LBP features. In Computing, Communication and Applications (ICCCA), 2012 International Conference on (pp. 1-6). IEEE.
23 Panigrahy, M. P., & Kumar, N. (2012). Face Recognition using Genetic Algorithm and Neural Networks. International Journal of Computer Applications, 55(4).
24 Raja, A. S., & JosephRaj, V. (2012). Neural network based supervised self organizing maps for face recognition. International Journal on Soft Computing, 3(3), 31.
25 Karande, K. J. (2012, October). Multiscale wavelet based edge detection and Independent Component Analysis (ICA) for Face Recognition. In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on (pp. 1-5). IEEE.
26 Sehgal, U., & Saini, S. Facial image recognition model implementation with artificial neural networks using dimensions reduction techniques pca.
27 Sharma, N., & Singh, S. Novel k-PCA based Face Recognition Method.
28 Karande, K. J., Talbar, S. N., & Inamdar, S. S. (2012, May). Face recognition using oriented Laplacian of Gaussian (OLOG) and independent component analysis (ICA). In Digital Information and Communication Technology and it's Applications (DICTAP), 2012 Second International Conference on (pp. 99-103). IEEE.
29 Ebied, R. M. (2012, May). Feature extraction using PCA and Kernel-PCA for face recognition. In Informatics and Systems (INFOS), 2012 8th International Conference on (pp. MM-72). IEEE.
30 Song, H. A., Choi, S. D., & Lee, S. Y. (2011, November). Enhanced discrimination of face orientation based on gabor filters. In Neural Information Processing (pp. 217-224). Springer Berlin Heidelberg.
31 Kashem, M. A., Ahmed, S., Akhter, M. N., & Alam, M. M. (2011). Automatically Face Detection and Recognition System Based on Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN). IJCIT, ISSN, 2078-5828.
32 Shylaja, S. S., Murthy, K. B., & Natarajan, S. (2011). Dimensionality reduction techniques for face recognition. Reviews, Refinements and New Ideas in Face Recognition, InTech, 141-166.
33 Kumar, S., & Banerji, S. (2011). Face recognition using K2DSPCA. In Proceedings of the International Conference on Information and Network Technology (pp. 84-88).
34 Abdu Rahiman, V., & Jiji, C. V. (2011). Face Hallucination using Eigen Transformation in Transform Domain. International Journal of Image Processing (IJIP), 3(6), 265.
35 Wandale, M. S. P., Tijare, P. A., & Sawalkar, S. N. Principal Component Analysis (PCA) with Back Propogation Neural Network (BPNN) for Face Recognition System.
36 Deboeverie, F., Veelaert, P., & Philips, W. (2011). Face analysis using curve edge maps. In Image Analysis and Processing–ICIAP 2011 (pp. 109-118). Springer Berlin Heidelberg.
37 Prasad, M. S. R. S., Panda, S. S., Deepthi, G., & Anisha, V. (2011). Face recognition using PCA and feed forward neural networks. International Journal of Computer Science and Telecommunications, 2(8), 79-82.
38 Kashem, M. A., Akhter, M. N., Ahmed, S., & Alam, M. M. (2011). Face recognition system based on principal component analysis (PCA) with back propagation neural networks (BPNN). Canadian Journal on Image Processing and Computer Vision, 2(4), 36-45.
39 Kathavarayan, R. S., & Karuppasamy, M. (2010). Preserving Global and Local Features for Robust Face Recognition under Various Noisy Environments. International Journal of Image Processing (IJIP), 3(6), 328.
40 Ramesha, K., Raja, K. B., Venugopal, K. R., & Patnaik, L. M. (2010). Template based mole detection for face recognition. International Journal of computer theory and Engineering, 2(5), 797.
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16 Free-Books-Online 
Aapo Hyvarinen, Juha Karhunen, Erkki Oja “Independent Component Analysis” Book by A Wiley Interscience Publication, John Wiley & sons, inc, New York.
Aapo Hyvärinen and Erkki Oja “Independent Component Analysis: Algorithms and Applications” Neural Networks Research Centre Helsinki University of Technology P.O. Box 5400, FIN-02015 HUT, Finland, Neural Networks, 13(4-5):411-430, 2000.
Asian face image database from Intelligent MultimediaLaboratory www.nova.postech.ac.kr / special / imdb /paper_pdf.pdf.
Bruce A. Draper, Kyungim Baek, Marian Stewart Bartlett, “Recognizing faces with PCA and ICA”, Computer Vision and Image Understanding 91 (2003) 115-137.
Chengjun Liu; Wechsler, H.” Independent component analysis of Gabor features for face recognition”, Neural Networks, IEEE Transactions , Volume 14, Issue 4, July 2003 Page(s): 919 - 928
H.K.Ekenel, B.Sankur, “Feature Selection in the Independent Component Subspace for Face Recognition”, Pattern Recognition Letters 25 (2004) 1377-1388.
Indian face database www.cs.umass.edu / ~vidit / face database.
Jiajin Lei, Chao Lu, “Face recognition by Spatiotemporal ICA using Facial Database Collected by AcSys FRS Discover System”, Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD’06).
Jiajin Lei, Chao Lu, “Fusion of ICA Spatial, Temporal and Localized Features for Face Recognition”, Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD’06).
Jian Yang, David Zhang, Jing-yu Yang, “Is ICA Significantly Better than PCA for Face Recognition?” Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05) 1550- 5499/05.
Machine Readable Travel Documents (MRTD). http://www.icao.int/mrtd/overview/overview.cfm.
Manesh Kokare, B.N.Chatterji and P K Biswas “Comparison of similarity metrics for texture image retrieval” International conference TENCON 2003, 571-574.
Marian Stewart Bartlett, Javier R. Movellan, Terrence J. Sejonowski, “Face Recognition by Independent Component Analysis”, IEEE Transactions on Neural Networks, vol-13, No-6, November 2002, PP 1450-1464.
R. Hietmeyer. Biometric identification promises fast and secure processing of airline passengers. The International Civil Aviation Organization Journal, 55(9):10–11, 2000.
R. M. Bolle, J. H. Connell, and N. K. Ratha, “Biometric perils and patches,” Pattern Recognition vol. 35, pp. 2727 – 2738, 2002.
Rafael C. Gonzalez and Richard E. Woods. “Digital image processing”, Second Edition, published by Pearson Education, 2003.
Professor Kailash Jagannath Karande
Sinhgad Institute of Technology, Lonavala - India
Dr. Sanjay N Talbar
SGGS IET Nanded - India