Title: A continuous GA+LDA for training set enhancement in illumination and scale invariant face recognition
Authors: Laika Karsili, Adnan Acan
Addresses: Eastern Mediterranean University, Computer Engineering Department, Ismet Inonu Bulvari, Gazimagusa, TRNC, Via Mersin 10, Turkey. ' Eastern Mediterranean University, Computer Engineering Department, Ismet Inonu Bulvari, Gazimagusa, TRNC, Via Mersin 10, Turkey
Abstract: The face recognition performance of linear discriminant analysis (LDA) is considerably reduced by illumination and scale changes. Actually, performance of LDA is significantly affected by the number of images of each individual in the training set. Hence, the idea presented in this paper is to generate new images with illumination and scale changes, from the available frontal images in the training set, and add to training set such that the recognition rate of LDA is maximised over both the training and test sets. A continuous genetic algorithm is used to find the optimal sets of illumination and scale change parameters that make the success of LDA invariant to these facial variations. Compared to the application of LDA over the initially given face database, experimental evaluations demonstrated that the performance of LDA is significantly enhanced over the test set for illumination and scale changes, without decreasing the success for other test cases.
Keywords: face recognition; linear discriminant analysis; LDA; genetic algorithms; GAs; scale invariance; illumination invariance; dimensionality reduction; biometrics.
International Journal of Reasoning-based Intelligent Systems, 2010 Vol.2 No.3/4, pp.257 - 264
Published online: 12 Nov 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article