Feature extraction and recognition of Japanese voiceless stop consonants by principal component analysis.

Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...

Localized Principal Component Analysis Learning for Face Feature Extraction and Recognition

We present a novel face feature extraction approach using localized Principal Component Analysis (PCA) learning in face recognition tasks. The localized PCA approach produces a set of fine-tuned feature specific masks from a constrained subset of the input distribution. This method is a guided-learning based on a set of pre-defined feature points over a short training sequence. The result is th...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis

Timing differences in articulation between voiced and voiceless stop consonants: an analysis of cine-MRI data

Laryngeal and supralaryngeal articulators coordinately work to produce speech sounds. In order to study differences in supralaryngeal manifestations of voiced and voiceless consonants, we compared the tongue movement during a minimal pair /agise/ and /akise/ using the fast scanning techniques of MRI movies. The result showed that the tongue displacement starts earlier in /k/ than in /g/ for man...

Novel Feature Extraction for Face Recognition using Multiscale Principal Component Analysis

A method of face recognition based on multiscale principal component analysis (MSPCA) is presented in this paper. Initially face area is extracted from the given face image using Adaboost face detection algorithm. From the face area, regions of interest such as eyes, nose and mouth part are extracted by dividing it along horizontal and vertical directions. Then MSPCA is employed on these region...