Journal ArticleDOI
A new LDA-based face recognition system which can solve the small sample size problem
TLDR
It is proved that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectorsderived in the original space using LDA.About:
This article is published in Pattern Recognition.The article was published on 2000-10-01. It has received 1447 citations till now. The article focuses on the topics: Linear discriminant analysis & Scatter matrix.read more
Citations
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Proceedings Article
Neighbourhood Components Analysis
TL;DR: A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
Journal ArticleDOI
A direct LDA algorithm for high-dimensional data — with application to face recognition
TL;DR: However, for a task with very high dimensional data such as images, the traditional LDA algorithm encounters several difficulties, and before LDA can be used to reduce dimensionality, another procedure has to be first applied for dimensionality reduction.
Journal ArticleDOI
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
TL;DR: A general tensor discriminant analysis (GTDA) is developed as a preprocessing step for LDA for face recognition and achieves good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database.
Proceedings ArticleDOI
Probabilistic Linear Discriminant Analysis for Inferences About Identity
TL;DR: This paper describes face data as resulting from a generative model which incorporates both within- individual and between-individual variation, and calculates the likelihood that the differences between face images are entirely due to within-individual variability.
Journal ArticleDOI
2D and 3D face recognition: A survey
TL;DR: This paper provides an ''ex cursus'' of recent face recognition research trends in 2D imagery and 3D model based algorithms and proposes possible future directions.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.