Authors: J. Sheeba Rani, D. Devaraj, R. Sukanesh
Addresses: Indian Institute of Space Science and Technology, Trivandrum-695 022, Kerala, India. ' Kalasalingam University, Srivilliputhoor-626 190, Tamil Nadu, India. ' Thyagarajar College of Engineering, Madurai-625 015, Tamil Nadu, India
Abstract: In this paper, an efficient face recognition system using wavelet transform (WT) and modular autoassociative neural network (AANN) is proposed. WT, which has superior feature representation capability in multiresolution space and also less sensitive to noise and variation to lighting condition, is used to extract the features. The AANN which perform identity mapping of input space is used to capture the distribution of the low resolution face data obtained from WT. To avoid over fitting, over training and small-sample effect problem, we construct separate AANN for each person. To evaluate the proposed scheme, experiments have been conducted using ORL database and Yale A database for three cases namely normal images, noisy images and occluded images. In all the three cases, the modular AANN scheme produces better recognition rate compared to PCA, LDA and kernel associative memory (KAM). In particular, the proposed method outperforms the other methods in the case of occluded images.
Keywords: face recognition; biometrics; wavelet transform; Haar wavelet; AANNs; autoassociative neural networks; feature representation; normal images; noisy images; occluded images.
International Journal of Biometrics, 2008 Vol.1 No.2, pp.231 - 252
Published online: 30 Aug 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article