User authentication via adapted statistical models of face images

Cardinaux, F., Sanderson, C. and Bengio, S. (2006) User authentication via adapted statistical models of face images. IEEE Transactions on Signal Processing, 54 1: 361-373. doi:10.1109/TSP.2005.861075


Author Cardinaux, F.
Sanderson, C.
Bengio, S.
Title User authentication via adapted statistical models of face images
Journal name IEEE Transactions on Signal Processing   Check publisher's open access policy
ISSN 1053-587X
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1109/TSP.2005.861075
Open Access Status Not yet assessed
Volume 54
Issue 1
Start page 361
End page 373
Total pages 13
Place of publication New York
Publisher IEEE
Language eng
Subject 010401 Applied Statistics
080109 Pattern Recognition and Data Mining
080104 Computer Vision
080106 Image Processing
Abstract It has been previously demonstrated that systems based on local features and relatively complex statistical models, namely, one-dimensional (1-D) hidden Markov models (HMMs) and pseudo-two-dimensional (2-D) HMMs, are suitable for face recognition. Recently, a simpler statistical model, namely, the Gaussian mixture model (GMM), was also shown to perform well. In much of the literature devoted to these models, the experiments were performed with controlled images (manual face localization, controlled lighting, background, pose, etc). However, a practical recognition system has to be robust to more challenging conditions. In this article we evaluate, on the relatively difficult BANCA database, the performance, robustness and complexity of GMM and HMM-based approaches, using both manual and automatic face localization. We extend the GMM approach through the use of local features with embedded positional information, increasing performance without sacrificing its low complexity. Furthermore, we show that the traditionally used maximum likelihood (ML) training approach has problems estimating robust model parameters when there is only a few training images available. Considerably more precise models can be obtained through the use of Maximum a posteriori probability (MAP) training. We also show that face recognition techniques which obtain good performance on manually located faces do not necessarily obtain good performance on automatically located faces, indicating that recognition techniques must be designed from the ground up to handle imperfect localization. Finally, we show that while the pseudo-2-D HMM approach has the best overall performance, authentication time on current hardware makes it impractical. The best tradeoff in terms of authentication time, robustness and discrimination performance is achieved by the extended GMM approach.
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Information Technology and Electrical Engineering Publications
 
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