Informatica

A major challenge in face recognition is handling large pose variations. Here, we proposed to tackle this challenge by a three step sparse representation based method: estimating the pose of an unseen non-frontal face image, generating its virtual frontal view using learned view-dependent dictionaries, and classifying the generated frontal view. It is assumed that for a specific identity, the representation coefficients based on the view dictionary are invariant to pose and view-dependent frontal view generation transformations are learned based on pair-wise supervised dictionary learning. Experiments conducted on FERET and CMU-PIE face databases depict the efficacy of the proposed method.

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.

In this article we propose a novel Wavelet Packet Decomposition (WPD)‐based modification of the classical Principal Component Analysis (PCA)‐based face recognition method. The proposed modification allows to use PCA‐based face recognition with a large number of training images and perform training much faster than using the traditional PCA‐based method. The proposed method was tested with a database containing photographies of 423 persons and achieved 82–89% first one recognition rate. These results are close to that achieved by the classical PCA‐based method (83–90%).