Abstract
The paper presents a novel framework to 3D face recognition that exploits region covariance matrices (RCMs), Gaussian mixture models (GMMs) and support vector machine (SVM) classifiers. The proposed framework first combines several 3D face representations at the feature level using RCM descriptors and then derives low-dimensional feature vectors from the computed descriptors with the unscented transform. By doing so, it enables computations in Euclidean space, and makes Gaussian mixture modeling feasible. Finally, a support vector classifier is used for identity inference. As demonstrated by our experimental results on the FRGCv2 and UMB databases, the proposed framework is highly robust and exhibits desirable characteristics such as an inherent mechanism for data fusion (through the RCMs), the ability to examine local as well as global structures of the face with the same descriptor, the ability to integrate domain-specific prior knowledge into the modeling procedure and consequently to handle missing or unreliable data.
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BibTeX (Download)
@inproceedings{krizajrobust,
title = {Robust 3D face recognition using adapted statistical models},
author = {Janez Kri\v{z}aj and Simon Dobri\v{s}ek and Vitomir \v{S}truc and Nikola Pave\v{s}i\'{c}},
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/ERK2013b.pdf},
year = {2013},
date = {2013-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK'13)},
abstract = {The paper presents a novel framework to 3D face recognition that exploits region covariance matrices (RCMs), Gaussian mixture models (GMMs) and support vector machine (SVM) classifiers. The proposed framework first combines several 3D face representations at the feature level using RCM descriptors and then derives low-dimensional feature vectors from the computed descriptors with the unscented transform. By doing so, it enables computations in Euclidean space, and makes Gaussian mixture modeling feasible. Finally, a support vector classifier is used for identity inference. As demonstrated by our experimental results on the FRGCv2 and UMB databases, the proposed framework is highly robust and exhibits desirable characteristics such as an inherent mechanism for data fusion (through the RCMs), the ability to examine local as well as global structures of the face with the same descriptor, the ability to integrate domain-specific prior knowledge into the modeling procedure and consequently to handle missing or unreliable data.
},
keywords = {3d face recognition, biometrics, covariance descriptor, face verification, FRGC, GMM, modeling, performance evaluation, region-covariance matrix},
pubstate = {published},
tppubtype = {inproceedings}
}