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%.
|Title of host publication||Advances in Visual Computing - 6th International Symposium, ISVC 2010, Proceedings|
|Number of pages||10|
|Publication status||Published - 2010|
|Event||6th International, Symposium on Visual Computing, ISVC 2010 - Las Vegas, NV, United States|
Duration: 2010 Nov 29 → 2010 Dec 1
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||6th International, Symposium on Visual Computing, ISVC 2010|
|City||Las Vegas, NV|
|Period||10/11/29 → 10/12/1|
Bibliographical noteFunding Information:
Acknowledgments. The authors wish to thank Ministry Of Science, Technology and Innovation Malaysia. This work is supported by the e-Science grant no. 01-02-01-SF0114.
The authors wish to thank Ministry Of Science, Technology and Innovation Malaysia. This work is supported by the e-Science grant no. 01-02-01-SF0114.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)