Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Fusion of PCA-Based and LDA-Based Similarity Measures for Face Verification | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 647597 12 pages doi 2010 647597 Research Article Fusion of PCA-Based and LDA-Based Similarity Measures for Face Verification Mohammad T. Sadeghi 1 Masoumeh Samiei 1 and Josef Kittler2 1 Signal Processing Research Group Department of Electrical and Computer Engineering Yazd University . Box 89195-741 Yazd Iran 2 Centre for Vision Speech and Signal Processing University of Surrey Guildford Surrey GU2 7XH UK Correspondence should be addressed to Mohammad T. Sadeghi Received 1 December 2009 Accepted 19 July 2010 Academic Editor Yingzi Du Copyright 2010 Mohammad T. Sadeghi et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The problem of fusing similarity measure-based classifiers is considered in the context of face verification. The performance of face verification systems using different similarity measures in two well-known appearance-based representation spaces namely Principle Component Analysis PCA and Linear Discriminant Analysis LDA is experimentally studied. The study is performed for both manually and automatically registered face images. The experimental results confirm that our optimised Gradient Direction GD metric within the LDA feature space outperforms the other adopted metrics. Different methods of selection and fusion of the similarity measure-based classifiers are then examined. The experimental results demonstrate that the combined classifiers outperform any individual verification algorithm. In our studies the Support Vector Machines SVMs and Weighted Averaging of similarity measures appear to be the best fusion rules. Another interesting achievement of the work is that although features derived from the LDA approach lead to better results than .
