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.

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. I.

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. I.

The Eigenfaces algorithm has long been a mainstay in the field of Face Recognition due to the high dimensionality of face images. While providing minimal reconstruction error, the Eigenface-based transform space de-emphasises high frequency information, effectively reducing the information available for classification. Methods such as Linear Discriminant Analysis (also known as Fisherfaces) allow the construction of subspaces which preserve the discriminatory information. In this article Multi-Scale techniques are used to partition the information contained in the frequency domain prior to dimensionality reduction. In this manner it is possible to increase the information available for classification and hence increase the discriminative performance of both Eigenfaces and Fisherfaces techniques. Motivated by biological systems, Gabor filters are a natural choice for such a partitioning scheme. However, a comprehensive filter-bank will dramatically increase the already high dimensionality of extracted features. In this article a new method for intelligently reducing the dimensionality of Gabor features is presented. The FRGC dataset of 3D face images is used to examine the performance of Gabor filter-banks for Face Recognition and to compare them against other Multi-Scale partitioning methods such as the Discrete Wavelet Transform and the Discrete Cosine Transform.

In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.

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This paper develops a novel face recognition technique