Tied Factor Analysis for Face Recognition across Large Pose Differences

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...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 30; číslo 6; s. 970 - 984
Hlavní autoři:	Prince, S.J.D., Warrell, J., Elder, J.H., Felisberti, F.M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.06.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Artificial Intelligence Biometry - methods Cameras Computer science; control theory; systems Computer vision Exact sciences and technology Face - anatomy & histology Face and gesture recognition Face recognition Facial Expression Factor analysis Factor Analysis, Statistical Humans Image databases Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Lighting Likelihood Functions Linear transformations Mathematical analysis Mathematical models Noise Noise measurement Pattern recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Pervasive computing Probes Recognition Reproducibility of Results Sensitivity and Specificity Studies Subtraction Technique System testing Vectors Vectors (mathematics) Computer vision Face and gesture recognition Factor analysis Probabilistic approach Face recognition Image processing Extraction process Gesture recognition Computing methodologies Pattern recognition Modeling face and gesture recognition Gaussian noise Linear transformation Facies Database Observation data Feature extraction Metric EM algorithm Pattern analysis Artificial intelligence Pattern extraction applications
ISSN:	0162-8828, 1939-3539
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2008.48