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@article{Inform-Struc_2009,

title = {Gabor-based kernel-partial-least-squares discrimination features for face recognition},

author = {Vitomir Štruc and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/gabor-basedkernel-partial-least-squaresdiscriminationfeaturesforfacerecognition/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Informatica (Vilnius)},

volume = {20},

number = {1},

pages = {115-138},

abstract = {The paper presents a novel method for the extraction of facial features based on the Gabor-wavelet representation of face images and the kernel partial-least-squares discrimination (KPLSD) algorithm. The proposed feature-extraction method, called the Gabor-based kernel partial-least-squares discrimination (GKPLSD), is performed in two consecutive steps. In the first step a set of forty Gabor wavelets is used to extract discriminative and robust facial features, while in the second step the kernel partial-least-squares discrimination technique is used to reduce the dimensionality of the Gabor feature vector and to further enhance its discriminatory power. For optimal performance, the KPLSD-based transformation is implemented using the recently proposed fractional-power-polynomial models. The experimental results based on the XM2VTS and ORL databases show that the GKPLSD approach outperforms feature-extraction methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA) or generalized discriminant analysis (GDA) as well as combinations of these methods with Gabor representations of the face images. Furthermore, as the KPLSD algorithm is derived from the kernel partial-least-squares regression (KPLSR) model it does not suffer from the small-sample-size problem, which is regularly encountered in the field of face recognition.},

keywords = {biometrics, face recogntiion, kernel partial least squares, kpca, lda, pca},

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}

}