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Eigenvectors of Covariance Matrix using Row Mean and Column Mean Sequences for Face Recognition
H. B. Kekre, Sudeep D. Thepede, Akshay Maloo
Pages - 42 - 51     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
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KEYWORDS
Data Mining, Decision tree, Neural Network, Blood platelet, Transfusion
ABSTRACT
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.
CITED BY (18)  
1 Nithya, B., Sankari, Y. B., Manikantan, K., & Ramachandran, S. (2015). Discrete Orthonormal Stockwell Transform Based Feature Extraction for Pose Invariant Face Recognition. Procedia Computer Science, 45, 290-299.
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6 Kekre, H. B., & Kulkarni, V. (2012, October). Speaker identification using feature vector reduction of row mean of different transforms. In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on (pp. 1-5). IEEE.
7 Kekre, H. B., Thepade, S., Dhamejani, K., Khandelwal, S., & Azmi, A. (2012). Performance Comparison of Assorted Color Spaces for Multilevel Block Truncation Coding based Face Recognition. International Journal of Computer Science and Information Security, 10(3), 58.
8 Tayal, Y., Singh, M. I., & Lamba, R. Automatic face detection using color based segmentation and face recognition using eigen face.
9 Kekre, H. B., Sarode, T. K., Natu, P. J., & Natu, S. J. (2011). Performance Comparison of Face Recognition using DCT and Walsh Transform with Full and Partial Feature Vector against KFCG VQ Algorithm. threshold, 4, 29.
10 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.
11 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.
12 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.
13 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.
14 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.
15 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.
16 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.
17 Kekre, H. B., Sarode, T., Natu, P., & Natu, S. (2010, September). Performance Comparison of Face Recognition Using DCT Against Face Recognition Using Vector Quantization Algorithms. In LBG, KPE, KMCG, KFCG” International Journal Of Image Processing (IJIP.
18 Kekre, H. B., Sarode, T. K., Natu, S. J., & Natu, P. J. (1733). Speaker Identification Using 2-D DCT, Walsh And Haar On Full And Block Spectrogram. International Journal on Computer Science and Engineering, 2(5), 2010.
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Dr. H. B. Kekre
- India
hbkekre@yahoo.com
Mr. Sudeep D. Thepede
- India
Mr. Akshay Maloo
- India