An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obta...

The problem of ranking features computed by principal component analysis (PCA) in N-class problems have been addressed by the multi-class discriminant principal component analysis (MDPCA) and the Fisher discriminability criterion (FDC). These methods are motivated by the fact that PCA components do not necessarily represent important discriminant directions to separate sample groups. Given a da...

This paper is concerned with an empirical comparison of three proposed indices of predictor variable potency: (1) the scaled weights of the first Fisher-type discriminant function, (2) the total group estimates of the correlations between each predictor variable and the first Fishertype function, and (3) the within-groups estimates of the correlations between each predictor variable and the fir...

Feature extraction is one of key technologies of the palmprint identification. In the light of the characteristics subspace palmprint identification technology, the two-dimensional principal component analysis, two-dimensional fisher linear discriminant and two-way two-dimensional principal component analysis algorithm is deeply analyzed. Based on two-dimensional subspace palmprint identificati...

Kernel trick is a powerful tool being used for solving complex pattern classification problem. As long as a linear feature extraction algorithm can be expressed exclusively by dot-products, it can be extended to non-linear version by combining kernel method. In this paper, we present such an improved iterative algorithm used for linear discriminant analysis. By mapping data onto high dimensiona...

Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we seek to learn the metric from data via kernel methods and multidimensional scaling (MDS) techniques. Under the classification setting, we define discriminant kernels on the joint space of input and output spaces and p...

Both two dimensional principal component analysis and fisher linear discriminant analysis are successful face recognition algorithms. Recognition rate, time complexity can be improved by combining the two algorithms with the very powerful tool discrete wavelet transform. Experiments on the ORL face database show that the proposed method outperforms PCA, LDA, DWT+LDA algorithms in terms of recog...

The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and e:cient than conventional PCA and FLD. ? 2002 Pat...