NDLI: On local features for GMM based face verification

Content Provider	IEEE Xplore Digital Library
Author	Sanderson, C. Saban, M. Yongsheng Gao
Copyright Year	2005
Description	Author affiliation: National ICT Australia, Canberra, ACT, Australia (Sanderson, C.)
Abstract	It has been recently shown that local feature approaches to face verification are considerably more robust than holistic approaches, in terms of translations (caused by automatic face localization) and pose variations. In this paper, we first investigate whether features based on local principal component analysis (LPCA) are more discriminative than features based on the 2D discrete cosine transform (2D DCT). We also investigate several methods for modifying the two feature extraction techniques in order to counteract the effects of linear and non-linear illumination changes, without losing discriminative information. Results on the XM2VTS database show that when using a Bayesian classifier based on Gaussian mixture models (GMMs), the performances of 2D DCT and LPCA techniques are quite similar, suggesting that the 2D DCT technique is preferable due to its lower computational complexity. When using 8/spl times/8 blocks, modifying the 2D DCT and LPCA techniques by removing the first coefficient, which is the most affected by illumination changes, enhances robustness with little change in discrimination ability; removing further coefficients causes a noticeable reduction in performance on clean images and provides little gain in robustness. When using the 2D DCT with 16/spl times/16 blocks, the first three coefficients need to be removed in order to achieve good robustness. It is further shown that contrary to previously published results, the use of deltas of low-order coefficients (to alleviate performance losses caused by removing coefficients) can adversely affect robustness.
Starting Page	650
Ending Page	655
File Size	243107
Page Count	6
File Format	PDF
ISBN	0769523161
DOI	10.1109/ICITA.2005.194
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-04
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Lighting Hidden Markov models Feature extraction Robustness Spatial databases Microelectronics Discrete cosine transforms Australia Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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