Linear subspace learning methods such as Fisher's Linear Discriminant Analysis (LDA), Unsupervised Discriminant Projection (UDP), and Locality Preserving Projections (LPP) have been widely used in face recognition applications as a tool to capture low dimensional discriminant information. However, when these methods are applied in the context of face recognition, they often encounter the small-...

Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...

One popular feature type in speech recognition is based on linear transformations of sequences of cepstral feature vectors. In general the transformation is generated in two steps: first a transformation like linear discriminant analysis (LDA) or heteroscedastic linear discriminant analysis (HLDA) is used to maximize separation between classes and reduce the dimensionality, followed by a decorr...

This paper investigates the use of the dimensionality-reduction techniques weighted linear discriminant analysis (WLDA), and weighted median fisher discriminant analysis (WMFD), before probabilistic linear discriminant analysis (PLDA) modeling for the purpose of improving speaker verification performance in the presence of high inter-session variability. Recently it was shown that WLDA techniqu...

An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623~1783 cm−1 were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination m...

Linear double-layered feature extraction (DFE) technique has recently appeared in radar automatic target recognition (RATR). This paper develops this technique to a nonlinear field via parallelizing a series of kernel Fisher discriminant (KFD) units, and proposes a novel kernel-based DFE algorithm, namely, multi-KFD-based linear discriminant analysis (MKFD-LDA). In the proposed method, a multiK...

In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performanc...

The performance of a local feature based system, using Gabor-filters, and a global template matching based system, using a combination of PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), was correlated with human performance on a recognition task involving 32 face images. Both systems showed qualitative similarities to human performance in that all but one of the calcu...

In this paper, the problem of frontal view recognition on still images is confronted, using subspace learning methods. The aim is to acquire the frontal images of a person in order to achieve better results in later face or facial expression recognition. For this purpose, we utilize a relatively new subspace learning technique, Clustering based Discriminant Analysis (CDA) against two well-known...

Biometric-based techniques have emerged for recognizing individuals instead of using passwords, PINs, smart cards, plastic cards, tokens etc for authenticating people. Automated face recognition has become a major field of interest. In this field several facial recognition algorithms have been explored in the past few decades. A face recognition system is expected to identify faces present in i...