Performance analysis of Linear appearance based algorithms for Face Recognition
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - Issue 2012 by IJCTT Journal | ||
| Volume-3 Issue-2 | ||
| Year of Publication : 2012 | ||
| Authors :Steven Lawrence Fernandes,Dr.G.Josemin Bala. |
Steven Lawrence Fernandes,Dr.G.Josemin Bala."Performance analysis of Linear appearance based algorithms for Face Recognition"International Journal of Computer Trends and Technology (IJCTT),V3(2):253-262 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we propose performance analysis of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) for face recognition. This analysis was carried out on various current PCA, LDA and LPP based face recognition algorithms using standard public databases. Among various PCA algorithms analyzed, Manual face localization used on ORL and SHEFFIELD database consisting of 100 components gives the best face recognition rate of 100%, the next best was 99.70% face recognition rate using PCA based Immune Networks (PCA-IN) on ORL database. Among various LDA algorithms analyzed, Illumination Adaptive Linear Discriminant Analysis (IALDA) gives the best face recognition rate of 98.9% on CMU PIE database, the next best was 98.125% using Fuzzy Fisherface through genetic algorithm on ORL database. Among various LPP algorithms analyzed, Subspace Discriminant LPP (SDLLP) provides the best face recognition rate of 98.38% on ORL database, the next best was 97.5% using Contourlet-based Locality Preserving Projection (CLPP) on ORL database.
References-
[1] Ming Yang, Jian-Wu Wan, and Gen-Lin Ji, "Random sampling LDA incorporating feature selection for face recognition," 2010 International Conference on Wavelet Analysis and Pattern Recognition, pp. 180 - 185, July 2010.
[2] Xiaoming Bai, and Chengzhang Wang, "Revised NMF with LDA based color face recognition," 2010 2nd International Conference on Networking and Digital Society, vol. 1, pp. 156 - 159, May 2010.
[3] Muhammad Imran Razzak, Muhammad Khurram Khan, Khaled Alghathbar, and Rubiyah Yousaf, "Face Recognition using Layered Linear Discriminant Analysis and Small Subspace," 2010 IEEE 10th International Conference on Computer and Information Technology, pp. 1407 - 1412, July 2010.
[4] Erwin Hidayat, Fajrian Nur A., Azah Kamilah Muda, Choo Yun Huoy, and Sabrina Ahmad, "A Comparative Study of Feature Extraction Using PCA and LDA for Face Recognition," 2011 7th International Conference on Information Assurance and Security, pp. 354 - 359, December 2011.
[5] Chandar K.P., Chandra M.M., Kumar M.R., Latha, B.S., "Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system," 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 64 - 69, July 2011.
Keywords —face recognition; Principal Component Analysis; Linear Discriminant Analysis; Locality Preserving Projections; PCA-Immune Network; Illumination Adaptive LDA; Fisher Discriminant; Subspace Discriminant LPP; Contourlet-based Locality Preserving Projection.