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@article{EV-Struc_2009,

title = {Using regression techniques for coping with the one-sample-size problem of face recognition},

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

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

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Electrotechnical Review},

volume = {76},

number = {1-2},

pages = {7-12},

abstract = {There is a number of face recognition paradigms which ensure good recognition rates with frontal face images. However, the majority of them require an extensive training set and degrade in their performance when an insufficient number of training images is available. This is especially true for applications where only one image per subject is at hand for training. To cope with this one-sample-size (OSS) problem, we propose to employ subspace projection based regression techniques rather than modifications of the established face recognition paradigms, such as the principal component or linear discriminant analysis, as it was done in the past. Experiments performed on the XM2VTS and ORL databases show the effectiveness of the proposed approach. Also presented is a comparative assessment of several regression techniques and some popular face 

recognition methods.},

keywords = {biometrics, face recognition, one sample size problem, regression techniques, small sample size},

pubstate = {published},

tppubtype = {article}

}

@article{JEI-Struc_2008,

title = {Face authentication using a hybrid approach},

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

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

doi = {10.1117/1.2885149},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Journal of Electronic Imaging},

volume = {17},

number = {1},

pages = {1-11},

abstract = {This paper presents a hybrid approach to face-feature extraction based on the trace transform and the novel kernel partial-least-squares discriminant analysis (KPA). The hybrid approach, called trace kernel partial-least-squares discriminant analysis (TKPA) first uses a set of fifteen trace functionals to derive robust and discriminative facial features and then applies the KPA method to reduce their dimensionality. The feasibility of the proposed approach was successfully tested on the XM2VTS database, where a false rejection rate (FRR) of 1.25% and a false acceptance rate (FAR) of 2.11% were achieved in our best-performing face-authentication experiment. The experimental results also show that the proposed approach can outperform kernel methods such as generalized discriminant analysis (GDA), kernel fisher analysis (KFA) and complete kernel fisher discriminant analysis (CKFA) as well as combinations of these methods with features extracted using the trace transform.},

keywords = {biometrics, face recognition, hybrid approach, kernel partial least squares, trace transform},

pubstate = {published},

tppubtype = {article}

}

@article{EV-Struc_2007,

title = {Impact of image degradations on the face recognition accuracy},

author = {Vitomir Štruc and Nikola Pavešić},

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

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {Electrotechnical Review},

volume = {74},

number = {3},

pages = {145-150},

abstract = {The accuracy of automatic face recognition systems depends on various factors among which robustness and accuracy of the face localization procedure, choice of an appropriate face-feature extraction procedure, as well as use of a suitable matching algorithm are the most important. Current systems perform relatively well whenever test images to be recognized are captured under conditions similar to those of the training images. However, they are not robust enough if there is a difference between test and training images. Changes in image characteristics such as noise, colour depth, background and compression all cause a drop in performance of even the best systems of today. At this point the main question is which image characteristics are the most important in terms of face recognition performance and how they affect the recognition accuracy. This paper addresses these issues and presents performance evaluation (Table 2.) of three popular subspace methods (PCA, LDA and ICA) using ten degraded versions of the XM2VTS face image database [10]. The presented experimental results show the effects of different changes in image characteristics on four score level fusion rules, namely, the maximum, minimum, sum and product rule. All of the feature extraction procedures as well as the fusion strategies are rather insensitive to the presence of noise, JPEG compression, colour depth reduction, and so forth, while on the other hand they all exhibit great sensitivity to degradations such as face occlusion and packet loss simulation},

keywords = {biometrics, face recognition, ica, image degradations, lda, pca},

pubstate = {published},

tppubtype = {article}

}

@article{TIFS_PrivacySurvey,

title = {Privacy-Enhancing Face Biometrics: A Comprehensive Survey},

author = {Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Srruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9481149

https://lmi.fe.uni-lj.si/en/visual_privacy_of_faces__a_survey_preprint-compressed/},

doi = {10.1109/TIFS.2021.3096024},

journal = {IEEE Transactions on Information Forensics and Security},

volume = {vol. 16},

pages = {4147-4183},

abstract = {Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible,  several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. },

keywords = {B-PETs, biometrics, DEID, deidentification, face deidentification, face recognition, FaceGEN, overview, privacy, privacy-enhancing techniques, survey},

pubstate = {published},

tppubtype = {article}

}