2009 |
Štruc, Vitomir; Pavešić, Nikola Gabor-based kernel-partial-least-squares discrimination features for face recognition Journal Article In: Informatica (Vilnius), vol. 20, no. 1, pp. 115-138, 2009. Abstract | Links | BibTeX | Tags: biometrics, face recogntiion, kernel partial least squares, kpca, lda, pca @article{Inform-Struc_2009,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. |
2008 |
Štruc, Vitomir; Mihelič, France; Pavešić, Nikola Face authentication using a hybrid approach Journal Article In: Journal of Electronic Imaging, vol. 17, no. 1, pp. 1-11, 2008. Abstract | Links | BibTeX | Tags: biometrics, face recognition, hybrid approach, kernel partial least squares, trace transform @article{JEI-Struc_2008,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. |