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@inproceedings{struc2015modest,

title = {Modest face recognition},

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

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

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

pages = {1--6},

publisher = {IEEE},

abstract = {The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework.},

keywords = {biometrics, face verification, Gabor features, image descriptors, LBP, multi modality, PaSC, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dobrivsek2015face,

title = {Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier},

author = {Simon Dobrišek and Vitomir Štruc and Janez Križaj and France Mihelič},

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

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): BWild 2015},

volume = {2},

pages = {1--6},

organization = {IEEE},

abstract = {The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image using a battery of Gabor filters, then reduces the dimensionality of the extracted feature vector by projecting it into a low-dimensional subspace and finally produces a representation suitable for identity inference by applying PLDA to the projected feature vector. The proposed approach extends the popular Gabor-Fisher Classifier (GFC) to a probabilistic setting and thus improves on the generalization capabilities of the GFC method. The PGFC technique is assessed in face verification experiments on the Point and Shoot Face Recognition Challenge (PaSC) database, which features real-world videos of subjects performing everyday tasks. Experimental results on this challenging database show the feasibility of the proposed approach, which improves on the best results on this database reported in the literature by the time of writing.},

keywords = {biometrics, BWild, FG, Gabor features, PaSC, plda, probabilistic Gabor Fisher classifier, probabilistic linear discriminant analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BTAS2009,

title = {Principal Gabor Filters for Face Recognition},

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

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

doi = {10.1109/BTAS.2009.5339020},

year  = {2009},

date = {2009-09-01},

urldate = {2009-09-01},

booktitle = {Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Systems and Applications (BTAS'09)},

pages = {1-6},

publisher = {IEEE},

address = {Washington D.C., U.S.A.},

abstract = {Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance of recognition techniques presented in the literature exploits these filters to achieve robust face recognition. However, while exhibiting desirable properties, such as orientational selectivity or spatial locality, Gabor filters have also some shortcomings which crucially affect the characteristics and size of the Gabor representation of a given face pattern. Amongst these shortcomings the fact that the filters are not orthogonal one to another and are, hence, correlated is probably the most important. This makes the information contained in the Gabor face representation redundant and also affects the size of the representation. To overcome this problem we propose in this paper to employ orthonormal linear combinations of the original Gabor filters rather than the filters themselves for deriving the Gabor face representation. The filters, named principal Gabor filters for the fact that they are computed by means of principal component analysis, are assessed in face recognition experiments performed on the XM2VTS and YaleB databases, where encouraging results are achieved.},

keywords = {biometrics, face verification, feature extraction, Gabor features, performance evaluation, principal Gabor filters},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICSPCS08,

title = {The phase-based Gabor Fisher classifier and its application to face recognition under varying illumination conditions},

author = {Vitomir Štruc and Boštjan Vesnicer and Nikola Pavešić},

doi = {10.1109/ICSPCS.2008.4813663},

isbn = {978-1-4244-4243-0},

year  = {2008},

date = {2008-12-01},

booktitle = {Proceedings of the IEEE International Conference on Signal Processing and Communication Systems (ICSPCS'08)},

pages = {1-6},

publisher = {IEEE},

address = {Gold Coast, Australia},

abstract = {The paper introduces a feature extraction technique for face recognition called the phase-based Gabor Fisher classifier (PBGFC). The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. The feasibility of the proposed method was assessed in a series of face verification experiments performed on the XM2VTS database. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as principal component analysis (PCA), the Fisherface method or the DCT-mod2 approach, while it ensures similar verification performance as the established Gabor Fisher Classifier (GFC). The results also show that the proposed phase-based Gabor Fisher classifier performs the best among the tested methods when severe illumination changes are introduced to the face images.},

keywords = {biometrics, face verification, feature extraction, Gabor features, performance evaluation, phase congruency features, phase features},

pubstate = {published},

tppubtype = {inproceedings}

}