Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance

Direct Neighborhood Discriminant Analysis for Face Recognition

Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method