Open AccessJournal Article
The New Face Recognition Technique With the use of PCA and LDA.
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TLDR
A new face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis) is described, which shows a significant improvement compared with original images were fed directly to the LDA classifier.Abstract:
Image recognition using various image classifiers is an active research area. In this paper we will describe a new face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis). The new method consists of two steps: first we project the face image from the original vector space into a face subspace by using PCA technique and finally we use LDA to obtain a linear classifier. The idea of combination of two methods proved enough good improvement in the generalization capability of LDA where only few samples per class were available. BY using ORL dataset we observed a significant improvement compared with original images were fed directly to the LDA classifier. The newly proposed hybrid classifier has provided a useful framework means for general image recognition tasks as well.read more
Citations
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Statistical Pattern Recognition
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The Face Recognition Method of the Two-direction Variation of 2DPCA
Yue Zeng,Dazheng Feng +1 more
TL;DR: The face recognition method of the two-direction variation of 2D PCA (TDV2DPCA) is proposed, which makes use of more discriminant information in the variation of 1-dimensional PCA, and reduces the coefficients for image presentation in the way of two direction dimensionality reduction.
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A Novel Edge Detection Method Based on PCA1
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Ontology based Image Semantics Recognition using Description Logics
Xu Chuanyun,Zhang Yang,Yang Dan +2 more
TL;DR: Aimed at the lack of methods to describe image semantics and map low level semantics to high level semantics, Hiberarchy Model of Image Semantic is designed to extract image features from inherent information and stratify image semantics according to abstract degree.
A Novel Edge Detection Method Based on PCA
TL;DR: In this paper, the authors proposed a method of edge detection based on principal component analysis (PCA), which transforms the original dataset into lower-dimensional feature data using Karhunen-Loeve transform.
References
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Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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On combining classifiers
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Journal ArticleDOI
Human and machine recognition of faces: a survey
TL;DR: A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.