Performance analysis of subspace LDA approach for face recognition

Authors:
Sheifali Gupta;O. P. Sahoo;Rupesh Gupta;Ajay Goel
Affiliations:
Department of ECE, Singhania University, Rajasthan, India;Department of Electronics & Communication Engineering, N.I.T. Kurukshetra, India;Department of Mechanical Engineering, Singhania University, Rajasthan, India;Department of Computer Science & Engineering, Singhania University, Rajasthan, India
Venue:
TELE-INFO'10 Proceedings of the 9th WSEAS international conference on Telecommunications and informatics
Year:
2010

Citing 3
Cited 0

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Verification Protocol and Statistical Performance Analysis for Face Recognition Algorithms

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face recognition using discriminant eigenvectors

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the performance of subspace LDA for face recognition is evaluated with ORL database using MATLAB. It is shown that as the number of training images per individual increases, success rate also goes on increasing but it also causes increase in processing time because size of training database increases. When the training images per individual are 5 or 6, it gives maximum success rate with optimized performance time. Also there is a proportionately high recognition rate when the eigenface space's dimension is small (40-60) and it is less when eigenface space's dimension is large (180-200). When only significant eigen vectors are used in subspace LDA with 5 or 6 training images, then it gives maximum success rate up to 92%.