Spectral Analysis for Face Recognition
نویسندگان
چکیده
Different eigenspace-based approaches have been proposed for the recognition of faces, i.e. eigenface, fisherface and Laplacianface. For fisherfaces, the original image space is reduced to an n-c dimensional subspace in which the standard LDA is carried out, where n is the number of training samples and c is the number of classes. In this paper, we present a spectral analysis of fisherface which shows that the initial PCA dimensionality reduction might be insufficient. The noise might not be completely eliminated. This is due to the fact that fisherface only takes into account the discriminating structure while ignores geometrical structure. Based on the theoretical analysis, we propose a new method, called enhanced fisherface, which takes into account the discriminating structure as well as the intrinsic geometrical structure. Experimental results show that the proposed approach is effective in improving the performance of face recognition.
منابع مشابه
Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...
متن کاملSupervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کاملFace Recognition using Eigenfaces , PCA and Supprot Vector Machines
This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...
متن کاملVideo-based face recognition in color space by graph-based discriminant analysis
Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining the three color component images. In this work, we consider grayscale image as well as color s...
متن کاملCorrelation between Auditory Spectral Resolution and Speech Perception in Children with Cochlear Implants
Background: Variability in speech performance is a major concern for children with cochlear implants (CIs). Spectral resolution is an important acoustic component in speech perception. Considerable variability and limitations of spectral resolution in children with CIs may lead to individual differences in speech performance. The aim of this study was to assess the correlation between auditory ...
متن کاملFace Recognition by Cognitive Discriminant Features
Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These...
متن کامل