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@inproceedings{krizajrobust,

title = {Robust 3D face recognition using adapted statistical models},

author = {Janez Križaj and Simon Dobrišek and Vitomir Štruc and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/robust3dfacerecognitionusingadaptedstatisticalmodels/},

year  = {2013},

date = {2013-09-20},

urldate = {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}

}

@inproceedings{PHD2008,

title = {Regression techniques versus discriminative methods for face recognition},

author = {Vitomir Štruc and France Mihelič and Rok Gajšek and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/regressiontechniquesversusdiscriminativemethodsforfacerecognition/},

year  = {2008},

date = {2008-10-01},

urldate = {2008-10-01},

booktitle = {Proceedings of the 9th international PhD Workshop on Systems and Control},

pages = {1-5},

address = {Izola, Slovenia},

abstract = {In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discriminative techniques still exhibit some shortcomings when compared to the expressive approaches. More specifically, they suffer from the so-called small sample size (SSS) problem which is regularly encountered in the field of face recognition and occurs when the sample dimensionality is larger than the number of available training samples per subject. In this type of problems, the discriminative techniques need modifications in order to be feasible, but even in their most elaborate forms require at least two training samples per subject. The expressive approaches, on the other hand, are not susceptible to the SSS problem and are thus applicable even in the most extreme case of the small sample size problem, i.e., when only one training sample per subject is available. Nevertheless, in this paper we will show that the recognition performance of the expressive methods can match (or in some cases surpass) that of the discriminative techniques if the expressive feature extraction approaches are used as multivariate regression techniques with a pre-designed response matrix that encodes the class membership of the training samples. The effectiveness of the regression techniques for face recognition is demonstrated in a series of experiments performed on the ORL database. Additionally a comparative assessment of the regression techniques and popular discriminative approaches is presented.},

keywords = {biometrics, face recognition, face verification, modeling, performance evaluation, regression techniques},

pubstate = {published},

tppubtype = {inproceedings}

}