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@inproceedings{Rot_FG2024,

title = {ASPECD: Adaptable Soft-Biometric Privacy-Enhancement Using Centroid Decoding for Face Verification},

author = {Peter Rot and Philipp Terhorst and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/03/PeterRot_FG2024.pdf},

year  = {2024},

date = {2024-05-28},

booktitle = {Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG)},

pages = {1-9},

abstract = {State-of-the-art face recognition models commonly extract information-rich biometric templates from the input images that are then used for comparison purposes and identity inference. While these templates encode identity information in a highly discriminative manner, they typically also capture other potentially sensitive facial attributes, such as age, gender or ethnicity. To address this issue, Soft-Biometric Privacy-Enhancing Techniques (SB-PETs) were proposed in the literature that aim to suppress such attribute information, and, in turn, alleviate the privacy risks associated with the extracted biometric templates. While various SB-PETs were presented so far, existing   approaches do not provide dedicated mechanisms to determine which soft-biometrics to exclude and which to retain. In this paper, we address this gap and introduce ASPECD, a modular framework designed to selectively suppress binary and categorical soft-biometrics based on users' privacy preferences. ASPECD consists of multiple sequentially connected components, each dedicated for privacy-enhancement of an individual soft-biometric attribute.  The proposed framework suppresses attribute information using a Moment-based Disentanglement process coupled with a centroid decoding procedure, ensuring that the privacy-enhanced templates are directly comparable to the templates in the original embedding space, regardless of the soft-biometric modality being suppressed. 

To validate the performance of ASPECD, we conduct experiments on a large-scale face dataset and with five state-of-the-art face recognition models, demonstrating the effectiveness of the proposed approach in suppressing single and multiple soft-biometric attributes. Our approach achieves a competitive privacy-utility trade-off compared to the state-of-the-art methods in scenarios that involve enhancing privacy w.r.t. gender and ethnicity attributes. Source code will be made publicly available.},

keywords = {deepfake, deepfakes, face, face analysis, face deidentification, face image processing, face images, face synthesis, face verification, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy, soft biometrics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Babnik_IWBF2024,

title = {AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation},

author = {Žiga Babnik and Fadi Boutros and Naser Damer and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/03/iwbf2024_fiq.pdf},

year  = {2024},

date = {2024-04-10},

urldate = {2024-04-10},

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

pages = {1-6},

abstract = {Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://github.com/LSIbabnikz/AI-KD.},

keywords = {ai, CNN, deep learning, face, face image quality assessment, face image quality estimation, face images, face recognition, face verification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK2016_sebastjan,

title = {Učenje podobnosti v globokih nevronskih omrežjih za razpoznavanje obrazov},

author = {Žiga Stržinar and Klemen Grm and Vitomir Štruc},

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

year  = {2016},

date = {2016-09-20},

urldate = {2016-09-20},

booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},

address = {Portorož, Slovenia},

abstract = {Učenje podobnosti med pari vhodnih slik predstavlja enega najpopularnejših pristopov k razpoznavanju na področju globokega učenja. Pri tem pristopu globoko nevronsko omrežje na vhodu sprejme par slik (obrazov) in na izhodu vrne mero podobnosti med vhodnima slikama, ki jo je moč uporabiti za razpoznavanje. Izračun podobnosti je pri tem lahko v celoti udejanjen z globokim omrežjem, lahko pa se omrežje uporabi zgolj za izračun predstavitve vhodnega para slik, preslikava iz izračunane predstavitve v mero podobnosti pa se izvede z drugim, potencialno primernejšim modelom. V tem prispevku preizkusimo 5 različnih modelov za izvedbo preslikave med izračunano predstavitvijo in mero podobnosti, pri čemer za poizkuse uporabimo lastno nevronsko omrežje. Rezultati naših eksperimentov na problemu razpoznavanja obrazov kažejo na pomembnost izbire primernega modela, saj so razlike med uspešnostjo razpoznavanje od modela do modela precejšnje.},

keywords = {biometrics, CNN, deep learning, difference space, face verification, LFW, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK2015Klemen,

title = {The pose-invariant similarity index for face recognition},

author = {Klemen Grm and Simon Dobrišek and Vitomir Štruc},

year  = {2015},

date = {2015-04-20},

booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},

address = {Portorož, Slovenia},

keywords = {biometrics, CNN, deep learning, deep models, face verification, similarity learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@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{beveridge2015report,

title = {Report on the FG 2015 video person recognition evaluation},

author = {Ross Beveridge and Hao Zhang and Bruce A Draper and Patrick J Flynn and Zhenhua Feng and Patrik Huber and Josef Kittler and Zhiwu Huang and Shaoxin Li and Yan Li and Vitomir Štruc and Janez Križaj and others},

url = {https://lmi.fe.uni-lj.si/en/reportonthefg2015videopersonrecognitionevaluation/},

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)},

volume = {1},

pages = {1--8},

organization = {IEEE},

abstract = {This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing.},

keywords = {biometrics, competition, face verification, FG, group evaluation, PaSC, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{krivzaj2014feasibility,

title = {A Feasibility Study on the Use of Binary Keypoint Descriptors for 3D Face Recognition},

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

url = {https://lmi.fe.uni-lj.si/en/afeasibilitystudyontheuseofbinarykeypointdescriptorsfor3dfacerecognition/},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {Proceedings of the Mexican Conference on Pattern Recognition (MCPR)},

pages = {142--151},

organization = {Springer},

abstract = {Despite the progress made in the area of local image descriptors in recent years, virtually no literature is available on the use of more recent descriptors for the problem of 3D face recognition, such as BRIEF, ORB, BRISK or FREAK, which are binary in nature and, therefore, tend to be faster to compute and match, while requiring signicantly less memory for storage than, for example, SIFT or SURF. In this paper, we try to close this gap and present a feasibility study on the use of these descriptors for 3D face recognition. Descriptors are evaluated on the three challenging 3D face image datasets, namely, the FRGC, UMB and CASIA. Our experiments show the binary descriptors ensure slightly lower verication rates than SIFT, comparable to those of the SURF descriptor, while being an order of magnitude faster than SIFT. The results suggest that the use of binary descriptors represents a viable alternative to the established descriptors.},

keywords = {3d face recognition, binary descriptors, biometrics, BRISK, CASIA, face verification, FREAK, FRGC, MCPR, ORB, performance evaluation, SIFT, SURF},

pubstate = {published},

tppubtype = {inproceedings}

}

@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{ERK2013_Struc,

title = {Exploiting representation plurality for robust and efficient face recognition},

author = {Vitomir Štruc and Jeneja Žganec Gros and Simon Dobrišek and Nikola Pavešić},

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

year  = {2013},

date = {2013-09-01},

urldate = {2013-09-01},

booktitle = {Proceedings of the 22nd Intenational Electrotechnical and Computer Science Conference (ERK'13)},

volume = {vol. B},

pages = {121--124},

address = {Portorož, Slovenia},

abstract = {The paper introduces a novel approach to face recognition that exploits plurality of representation to achieve robust face recognition. The proposed approach was submitted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the submitted approach, elaborate on the results of the competition and, most importantly, present some general findings made during our development work that are of relevance to the broader (face recognition) research community.},

keywords = {competition, erk, face recognition, face verification, group evaluation, ICB, mobile biometrics, MOBIO, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{FG2013,

title = {Combining 3D face representations using region covariance descriptors and statistical models},

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

url = {https://lmi.fe.uni-lj.si/en/combining3dfacerepresentationsusingregioncovariancedescriptorsandstatisticalmodels/},

year  = {2013},

date = {2013-05-01},

urldate = {2013-05-01},

booktitle = {Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (IEEE FG), Workshop on 3D Face Biometrics},

publisher = {IEEE},

address = {Shanghai, China},

abstract = {The paper introduces a novel framework for 3D face recognition that capitalizes on region covariance descriptors and Gaussian mixture models. The framework presents an elegant and coherent way of combining multiple facial representations, while simultaneously examining all computed representations at various levels of locality. The framework first computes a number of region covariance matrices/descriptors from different sized regions of several image representations and then adopts the unscented transform to derive low-dimensional feature vectors from the computed descriptors. By doing so, it enables computations in the Euclidean space, and makes Gaussian mixture modeling feasible. In the last step a support vector machine classification scheme is used to make a decision regarding the identity of the modeled input 3D face image. The proposed framework exhibits several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrices), the ability to examine the facial images at different levels of locality, and the ability to integrate domain-specific prior knowledge into the modeling procedure. We assess the feasibility of the proposed framework on the Face Recognition Grand Challenge version 2 (FRGCv2) database with highly encouraging results.},

keywords = {3d face recognition, biometrics, covariance descriptors, face recognition, face verification, FG, gaussian mixture models, GMM, unscented transform},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vstruc2013patch,

title = {Patch-wise low-dimensional probabilistic linear discriminant analysis for Face Recognition},

author = {Vitomir Štruc and Nikola Pavešić and Jerneja Žganec-Gros and Boštjan Vesnicer},

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

doi = {10.1109/ICASSP.2013.6638075},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {2352--2356},

organization = {IEEE},

abstract = {The paper introduces a novel approach to face recognition based on the recently proposed low-dimensional probabilistic linear discriminant analysis (LD-PLDA). The proposed approach is specifically designed for complex recognition tasks, where highly nonlinear face variations are typically encountered. Such data variations are commonly induced by changes in the external illumination conditions, viewpoint changes or expression variations and represent quite a challenge even for state-of-the-art techniques, such as LD-PLDA. To overcome this problem, we propose here a patch-wise form of the LDPLDA technique (i.e., PLD-PLDA), which relies on local image patches rather than the entire image to make inferences about the identity of the input images. The basic idea here is to decompose the complex face recognition problem into simpler problems, for which the linear nature of the LD-PLDA technique may be better suited. By doing so, several similarity scores are derived from one facial image, which are combined at the final stage using a simple sum-rule fusion scheme to arrive at a single score that can be employed for identity inference. We evaluate the proposed technique on experiment 4 of the Face Recognition Grand Challenge (FRGCv2) database with highly promising results.},

keywords = {biometrics, face verification, FRGC, ICASSP, patch-wise approach, plda, probabilistic linear discriminant analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gunther20132013,

title = {The 2013 face recognition evaluation in mobile environment},

author = {Manuel Günther and Artur Costa-Pazo and Changxing Ding and Elhocine Boutellaa and Giovani Chiachia and Honglei Zhang and Marcus Assis de Angeloni and Vitomir Štruc and Elie Khoury and Esteban Vazquez-Fernandez and others},

url = {https://lmi.fe.uni-lj.si/en/the2013facerecognitionevaluationinmobileenvironment/},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

booktitle = {Proceedings of the IAPR International Conference on Biometrics (ICB)},

pages = {1--7},

organization = {IAPR},

abstract = {Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.},

keywords = {biometrics, competition, face recognition, face verification, group evaluation, mobile biometrics, MOBIO, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICIAR2010_Sparse,

title = {Face recogniton from color images using sparse projection analysis},

author = {Vitomir Štruc and Nikola Pavešić},

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

year  = {2010},

date = {2010-06-01},

urldate = {2010-06-01},

booktitle = {Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010)},

pages = {445-453},

address = {Povoa de Varzim, Portugal},

abstract = {The paper presents a novel feature extraction technique for face recognition which uses sparse projection axes to compute a lowdimensional representation of face images. The proposed technique derives the sparse axes by first recasting the problem of face recognition as a regression problem and then solving the new (under-determined) regression problem by computing the solution with minimum L1 norm. The developed technique, named Sparse Projection Analysis (SPA), is applied to color as well as grey-scale images from the XM2VTS database and compared to popular subspace projection techniques (with sparse and dense projection axes) from the literature. The results of the experimental assessment show that the proposed technique ensures promising results on un-occluded as well occluded images from the XM2VTS database.},

keywords = {biometrics, face verification, ICIAR, performance evaluation, sparse projection analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICASSP2010,

title = {Removing Illumination Artifacts from Face Images using the Nuisance Attribute Projection},

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

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

doi = {10.1109/ICASSP.2010.5495203},

year  = {2010},

date = {2010-03-01},

urldate = {2010-03-01},

booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'10)},

pages = {846-849},

publisher = {IEEE},

address = {Dallas, Texas, USA},

abstract = {Illumination induced appearance changes represent one of the open challenges in automated face recognition systems still significantly influencing their performance. Several techniques have been presented in the literature to cope with this problem; however, a universal solution remains to be found. In this paper we present a novel normalization scheme based on the nuisance attribute projection (NAP), which tries to remove the effects of illumination by projecting away multiple dimensions of a low dimensional illumination subspace. The technique is assessed in face recognition experiments performed on the extended YaleB and XM2VTS databases. Comparative results with state-of-the-art techniques show the competitiveness of the proposed technique.},

keywords = {biometrics, face recognition, face verification, illumination changes, illumination invariance, nuisance attribute projection, robust recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICPR_Struc_2010,

title = {Confidence Weighted Subspace Projection Techniques for Robust Face Recognition in the Presence of Partial Occlusions},

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

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

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR'10)},

pages = {1334-1338},

address = {Istanbul, Turkey},

keywords = {biometrics, face recognition, face verification, ICPR, performance evaluation, subspace projection},

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{ERK2009N,

title = {Nuisance Attribute Projection in the Logarithm Domain for Face Recognition under Severe Illumination Changes},

author = {Vitomir Štruc and Zongmin Ma and Nikola Pavešić},

year  = {2009},

date = {2009-09-01},

booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09)},

pages = {279-281},

address = {Portorož, Slovenia},

keywords = {biometrics, face verification, illumination changes, illumination invariance, nuisance attribute projection, performance evaluation, robust recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK2009S,

title = {Face Recognition using Sparse Projection Axes},

author = {Vitomir Štruc and Zongmin Ma and Nikola Pavešić},

year  = {2009},

date = {2009-09-01},

booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'09)},

pages = {271-274},

address = {Portorož, Slovenia},

keywords = {biometrics, erk, face recognition, face verification, performance evaluation, sparse projection analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{FSKD208b,

title = {A comparative assessment of appearance based feature extraction techniques and their susceptibility to image degradations in face recognition systems},

author = {Vitomir Štruc and Nikola Pavešić},

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

year  = {2009},

date = {2009-06-01},

urldate = {2009-06-01},

booktitle = {Proceedings of the International Conference on Machine Learning and Pattern Recognition (ICMLPR'09)},

volume = {54},

pages = {326-334},

address = {Paris, France},

abstract = {Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in ”real-life” operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.},

keywords = {biometrics, face recognition, face verification, image degradations, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mathmod09,

title = {A comparison of feature normalization techniques for PCA-based palmprint recognition},

author = {Vitomir Štruc and Nikola Pavešić},

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

year  = {2009},

date = {2009-02-01},

urldate = {2009-02-01},

booktitle = {Proceedings of the International Conference on Mathematical Modeling (MATHMOD'09)},

pages = {2450-2453},

address = {Viena, Austria},

abstract = {Computing user templates (or models) for biometric authentication systems is one of the most crucial steps towards efficient and accurate biometric recognition. The constructed templates should encode user specific information extracted from a sample of a given biometric modality, such as, for example, palmprints, and exhibit a sufficient level of dissimilarity with other templates stored in the systems database. Clearly, the characteristics of the user templates depend on the approach employed for the extraction of biometric features, as well as on the procedure used to normalize the extracted feature vectors. While feature-extraction methods are a well studied topic, for which a vast amount of comparative studies can be found in the literature, normalization techniques lack such studies and are only briefly mentioned in most cases. In this paper we, therefore, apply several normalization techniques to feature vectors extracted from palmprint images by means of principal component analysis (PCA) and perform a comparative analysis on the results. We show that the choice of an appropriate normalization technique greatly influences the performance of the palmprint-based authentication system and can result in error rate reductions of more than 30%.},

keywords = {biometrics, face verification, feature normalization, normalization, pca, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ISPRA09,

title = {Image normalization techniques for robust face recognition},

author = {Vitomir Štruc and Nikola Pavešić},

year  = {2009},

date = {2009-02-01},

booktitle = {Proceedings of the International Conference on Signal Processing, Robotics and Automation (ISPRA'09)},

pages = {155-160},

address = {Cambridge, UK},

keywords = {biometrics, face recognition, face verification, histogram remapping, performance evaluation, preprocessing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICB2009,

title = {Face Video Competition},

author = {Norman Poh and Chi Ho Chan and Josef Kittler and Sebastien Marcel and Christopher McCool and Enrique Argones-Rua and Jose Luis Alba-Castro and Mauricio Villegas and Roberto Paredes and Vitomir Štruc and Nikola Pavešić and Albert Ali Salah and Hui Fang and Nicholas Costen},

editor = {Massimo Tistarelli and Mark Nixon},

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

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

booktitle = {Proceedings of the international Conference on Biometrics (ICB)},

volume = {5558},

pages = {715-724},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

series = {Lecture Notes on Computer Science},

abstract = {Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.},

keywords = {biometrics, competition, face recognition, face verification, ICB, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BioID_Multi2009,

title = {Illumination Invariant Face Recognition by Non-Local Smoothing},

author = {Vitomir Štruc and Nikola Pavešić},

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

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

booktitle = {Biometric ID management and multimodal communication},

volume = {5707},

pages = {1-8},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

series = {Lecture Notes on Computer Science},

abstract = {Existing face recognition techniques struggle with their performance when identities have to be determined (recognized) based on image data captured under challenging illumination conditions. To overcome the susceptibility of the existing techniques to illumination variations numerous normalization techniques have been proposed in the literature. These normalization techniques, however, still exhibit some shortcomings and, thus, offer room for improvement. In this paper we identify the most important weaknesses of the commonly adopted illumination normalization techniques and presents two novel approaches which make use of the recently proposed non-local means algorithm. We assess the performance of the proposed techniques on the YaleB face database and report preliminary results.},

keywords = {biometrics, face verification, illumination changes, illumination invariance, illumination normalization, pca, preprocessing},

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}

}

@inproceedings{FSKD208b,

title = {The corrected normalized correlation coefficient: a novel way of matching score calculation for LDA-based face verification},

author = {Vitomir Štruc and Nikola Pavešić},

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

doi = {10.1109/FSKD.2008.334},

isbn = {978-0-7695-3305-6},

year  = {2008},

date = {2008-10-01},

urldate = {2008-10-01},

booktitle = {Proceedings of the IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'08)},

volume = {4},

pages = {110-115},

publisher = {IEEE},

address = {Jinan, China},

abstract = {The paper presents a novel way of matching score calculation for LDA-based face verification. Different from the classical matching schemes, where the decision regarding the identity of the user currently presented to the face verification system is made based on the similarity (or distance) between the "live" feature vector and the template of the claimed identity, we propose to employ a measure we named the corrected normalized correlation coefficient, which considers both the similarity with the template of the claimed identity as well as the similarity with all other templates stored in the database. The effectiveness of the proposed measure was assessed on the publicly available XM2VTS database where encouraging results were achieved.},

keywords = {biometrics, face verification, lda, matching score calculation},

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}

}

@inproceedings{ERK2008,

title = {Combining experts for improved face verification performance},

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

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

year  = {2008},

date = {2008-09-01},

urldate = {2008-09-01},

booktitle = {Proceedings of the IEEE International Electrotechnical and Computer Science Conference (ERK'08)},

pages = {233-236},

address = {Portorož, Slovenia},

abstract = {Samodejno razpoznavanje (avtentikacija/identifikacija) obrazov predstavlja eno najaktivnejših raziskovalnih področij biometrije. Avtentikacija oz. identifikacija oseb z razpoznavanjem obrazov ponuja možen način povečanja varnosti pri različnih dejavnostih, (npr. pri elektronskem poslovanju na medmrežju, pri bančnih storitvah ali pri vstopu v določene prostore, stavbe in države). Ponuja univerzalen in nevsiljiv način razpoznavanja oseb, ki pa trenutno še ni dovolj zanesljiv. Kot možna rešitev problema zanesljivosti razpoznavanja se v literaturi vse pogosteje pojavljajo večmodalni pristopi, v katerih se razpoznavanje izvede na podlagi večjega števila postopkov razpoznavanja obrazov. V skladu z opisanim trendom, bomo v članku ovrednotili zanesljivost delovanja različnih postopkov razpoznavanja obrazov, ki jih bomo na koncu združili še v večmodalni pristop. S pomočjo eksperimentov na podatkovni zbirki XM2VTS bomo preverili zanesljivost delovanja večmodalnega pristopa in jo primerjali z zanesljivostjo uveljavljenih postopkov razpoznavanja.},

keywords = {biometrics, erk, face recognition, face verification, fusion, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}