Abstract
In this paper a high performance face recognition system, based on different data fusion techniques combining the decisions obtained from the probability distribution functions (PDF) based face recognition system in different colour channels, is introduced. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the Kullback- Leibler Distance (KLD) between the PDF of a given face and the PDFs of faces in the database. Well known data fusion techniques such as Median Rule, Sum Rule, Max Rule, Product Rule, Majority Voting (MV) and feature vector fusion (FVF) have been employed to increase the recognition performance. The proposed system has been tested on the FERET, Head Pose, Essex University, and Georgia Tech University face databases. The overall results indicate that the median rule over-performs the other fusion techniques
References
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- M. Nilsson, J. Nordberg, and I. Claesson, " Face Detection using Local SMQT Features and Split up Snow Classifier ", ICASSP 2007, Vol. 2, pp: II-589 – II-592. International
Abstract
References
- W.W. Bledsoe, “The Model Method in Facial Recognition”, Panoramic Research Inc., Palo Alto, Cal., and Rep. PRI: 15, CA, 1964. (1)
- H. Demirel, and G. Anbarjafari, "Pose Invariant Face Recognition Using Image Histograms",
- Conference On Computer Vision Theory and Applications (VISAPP 2008), Portugal, Vol: 2, pp: 282-285.
- I. Laptev, “Improvements of Object Detection Using Boosted Histograms”, BMVC, 2006, pp: III: 949-958.
- Tae-Woong Yoo, Il-Seok Oh, “A fast algorithm for tracking human faces based on chromatic PDFs”, Pattern Recognition Letters, vol. 20, 967-978, 1999.
- Y. Rodriguez and S. Marcel, “Face Authentication Using Adapted Local Binary Pattern PDFs”. Proceedings of the 9th European Conference on Computer Vision (ECCV), pages 321-332, Graz, Austria, May 7- 13 2006.
- H. Demirel, and G. Anbarjafari, "Pose Invariant Face Recognition Using Probability Distribution Functions in Different Color Channels", IEEE Signal Processing Letter, Vol. 15, 2008, pp: 537-540.
- M. Nilsson, J. Nordberg, and I. Claesson, " Face Detection using Local SMQT Features and Split up Snow Classifier ", ICASSP 2007, Vol. 2, pp: II-589 – II-592. International
Details
| Other ID | JA42BA43VZ |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | December 1, 2012 |
| Published in Issue | Year 2012 Volume: 2 Issue: 2 |