Tied factor analysis for face recognition across large pose differences

Prince, Simon J.D., Elder, James H., Warrell, Jonathan and Felisberti, Fatima M. (2008) Tied factor analysis for face recognition across large pose differences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), pp. 970-984. ISSN (print) 0162-8828

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Abstract

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches.

Item Type: Article
Additional Information: Collaboration between UCL and Kingston University. This work was supported by Engineering and Physical Sciences Research Council (EPSRC); OCE-Etech; GEOIDE and PRECARN.
Uncontrolled Keywords: computing methodologies, pattern recognition, applications, face and gesture recognition, algorithms, models, information technology
Research Area: Psychology
Faculty, School or Research Centre: Faculty of Arts and Social Sciences > School of Social Science (until November 2012)
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Depositing User: Fatima Maria Felisberti
Date Deposited: 27 Jun 2008
Last Modified: 16 Jul 2012 21:47
URI: http://eprints.kingston.ac.uk/id/eprint/3144

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