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Eigenvectors of Covariance Matrix using Row Mean and Column Mean Sequences for Face Recognition
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International Journal of Biometrics and Bioinformatics (IJBB)
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Volume:  4    Issue:  2
Pages:  13-99
Publication Date:   May 2010
ISSN (Online): 1985-2347
Pages 
42 - 51
Author(s)  
H. B. Kekre - India
 
Published Date   
10-06-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Data Mining, Decision tree, Neural Network, Blood platelet, Transfusion 
 
 
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Face recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of face recognition algorithms have been developed from decades. Principal Component Analysis (PCA) [2][3] is one of the most successful techniques that has been used in face recognition. Four criteria for image pixel selection to create feature vector were analyzed: the first one has all the pixels considered by converting the image into gray plane, the second one is based on taking row mean in RGB plane of face image, the third one is based on taking column mean in RGB plane finally, the fourth criterion is based on taking row and column mean of face image in RGB plane and feature vector were generated to apply PCA technique. Experimental tests on the ORL Face Database [1] achieved 99.60% of recognition accuracy, with lower computational cost. To test the ruggedness of proposed techniques, they are tested on our own created face database where 80.60% of recognition accuracy is achieved. For a 128 × 128 image that means that one must compute a 16384 x 16384 matrix and calculate 16,384 eigenfaces. Computationally, this is not very efficient as most of those eigenfaces are not useful for our task. Using row mean and column mean reduces computations resulting in faster face recognition with nearly same accuracy. 
 
 
 
1 AT&T Laboratories, Cambridge, UK. “The ORL Database of Faces” (now AT&T “The Database of Faces”). Available [Online: http://www.cl.cam.ac.uk/Research/ DTG/attarchive/ pub/data/att_faces.zip Last referred on 15 September 2007].
2 M.Turk and A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive Neuroscience, March 1991.
3 M.A. Turk and A.P. Pentland. “Face recognition using eigenfaces”. In Proc. of Computer Vision and Pattern Recognition, pages 586-591. IEEE,June 1991b.
4 L.Sirovich and M. Kirby (1987). "Low-dimensional procedure for the characterization of human faces". Journal of the Optical Society of America A4: 519–524.
5 H.B.Kekre, TanujaSarode, Sudeep D. Thepade, “DCT Applied to Row Mean and Column Vectors in Fingerprint Identification”, In Proceedings of Int. Conf. on Computer Networks and Security (ICCNS), 27-28 Sept. 2008, VIT, Pune.
6 Prof. Y. VijayaLata, Chandra KiranBharadwajTungathurthi, H. Ram Mohan Rao, Dr. A. Govardhan, Dr. L. P. Reddy, "Facial Recognition using Eigenfaces by PCA", In International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May 2009, Pages 587-590.
7 H. B. Kekre, Kamal Shah, Tanuja K. Sarode, Sudeep D. Thepade, ”Performance Comparison of Vector Quantization Technique – KFCG with LBG, Existing Transforms and PCA for Face Recognition”, Int. Journal of Information Retrieval (IJIR), Vol. 02, Issue 1, pp.: 64-71, 2009.
8 H.B.Kekre, Sudeep D. Thepade, ArchanaAthawale, Anant Shah, PrathmeshVerlekar, SurajShirke, “Walsh Transform over Row Mean and Column Mean using Image Fragmentation and Energy Compaction for Image Retrieval”, International Journal on Computer Science and Engineering (IJCSE),Volume 2S, Issue1, January 2010, (ISSN: 0975–3397). Available online at www.enggjournals.com/ijcse.
9 H.B.Kekre, Sudeep D. Thepade, ArchanaAthawale, Anant Shah, PrathmeshVerlekar, SurajShirke,“Energy Compaction and Image Splitting for Image Retrieval using Kekre Transform over Row and Column Feature Vectors”, International Journal of Computer Science and Network Security (IJCSNS),Volume:10, Number 1, January 2010, (ISSN: 1738-7906) Available at www.IJCSNS.org.
10 H.B.Kekre, Sudeep D. Thepade, ArchanaAthawale, Anant Shah, PrathmeshVerlekar, SurajShirke, “Performance Evaluation of Image Retrieval using Energy Compaction and Image Tiling over DCT Row Mean and DCT Column Mean”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), BabasahebGawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.
11 H.B.Kekre, Sudeep D. Thepade, AkshayMaloo “Performance Comparison for Face Recognition using PCA, DCT & Walsh Transform of Row Mean and Column Mean”, ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 10, Issue II, Jun.2010, pp.9-18, Available online at http://209.61.248.177/gvip/ Volume10/Issue2/P1181012028.pdf.
12 D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 831–836, 1996.
13 C. Liu and H. Wechsler, “Evolutionary pursuit and its application to face recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 570–582, June 2000.
14 A. M. Martnez and A. C. Kak, “PCA versus LDA”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, 2001.May
15 http://www.webopedia.com/TERM/F/false_rejection.html
16 http://www.webopedia.com/TERM/F/false_acceptance.html
 
 
 
1 H. B. Kekre, T. K. Sarode, P. J. Natu and S. J. Natu, “Performance Comparison of Face Recognition using DCT and Walsh Transform with Full and Partial Feature Vector Against KFCG VQ Algorithm ” in IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (5), 2011, pp. 22-29.
2 H. B. Kekre, S. D. Thepade and A. Maloo, “CBIR Feature Vector Dimension Reduction with Eigenvectors of Covariance Matrix using Row, Column and Diagonal Mean Sequences ” International Journal of Computer Applications, 3 (12), pp. 9–46, July 2010.
3 H. B. Kekre, T. K. Sarode, S. J. Natu and P. J. Natu, “Performance Comparison Of 2-D DCT On Full/Block Spectrogram And 1-D DCT on Row Mean of Spectrogram for Speaker Identification” International Journal of Biometrics and Bioinformatics (IJBB), 4(3), pp. 100 – 112, July 2010.
4 Dr. H. B. Kekre, Dr. T. K. Sarode, Shachi J. Natu and Prachi J. Natu, “Performance Comparison of Speaker Identification Using DCT, Walsh, Haar on Full and Row Mean of Spectrogram” International Journal of Computer Applications, 5(6), pp. 30-37, August 2010.
5 Dr. H. B. Kekre, Dr. T. K. Sarode, S. J. Natu and P. J. Natu, “Speaker Identification Using 2-D DCT, Walsh And HAAR on Full and Block Spectrogram” International Journal on Computer Science and Engineering, 02(05), pp. 1733-1740, 2010.
6 Dr. H. B. Kekre, Dr. T. K. Sarode, P. J. Natu and S. J. Natu, “Performance Comparison of Face Recognition using DCT and Walsh Transform with Full and Partial Feature Vector against KFCG VQ Algorithm” in IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET), (5), 2011, pp. 22-29.
7 Dr. H. B. Kekre, V. Kulkarni, S. Venkatraman, A. Priya and S. Narasimhan, “Speaker Identification using Row Mean of DCT and Walsh Hadamard Transform” International Journal on Computer Science and Engineering (IJCSE), 3 (3), pp. 1295-1301, March 2011.
8 S. J. Natu, P. J. Natu, T. K. Sarode and H. B. Kekre, “Performance Comparison of Face Recognition Using DCT Against Face Recognition Using Vector Quantization Algorithms LBG, KPE, KMCG, KFCG” International Journal of Image Processing (IJIP), 4(4), pp. 377 – 389, October 2010.
 
 
 
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H. B. Kekre : Colleagues
Sudeep D. Thepede : Colleagues
Akshay Maloo : Colleagues  
 
 
 
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