A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Method for Human Face Recognition Using Wavelet Transform and Zernike Moments
CGIV '04 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
An analysis of facial expression recognition under partial facial image occlusion
Image and Vision Computing
A Study on Optimal Face Ratio for Recognition Using Part-Based Feature Extractor
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Why Is Facial Occlusion a Challenging Problem?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Face recognition under occlusions and variant expressions with partial similarity
IEEE Transactions on Information Forensics and Security
Weighted Fisher Non-negative Matrix Factorization for Face Recognition
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
Construction and Application of SVM Model and Wavelet-PCA for Face Recognition
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 01
FARO: FAce Recognition Against Occlusions and Expression Variations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
This paper aims to integrate part-based feature extractor, namely Non-negative matrix factorization (NMF), Local NMF and Spatially Confined NMF in wavelet frequency domain. Wavelet transform, with its approximate decomposition is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. 75% ratio of full-face images are used for training and testing since they contain sufficient information as reported in a previous study. Our experiments on Essex-94 Database demonstrate that feature extractors in wavelet frequency domain perform better than without any filters. The optimum result is obtained for SFNMF of r* = 60 with Symlet orthonormal wavelet filter of order 2 in the second decomposition level. The recognition rate is equivalent to 98%.