Journal ArticleDOI
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition
TLDR
A new LDA method is proposed that attempts to address the SSS problem using a regularized Fisher's separability criterion and a scheme of expanding the representational capacity of face database is introduced to overcome the limitation that the LDA-based algorithms require at least two samples per class available for learning.About:
This article is published in Pattern Recognition Letters.The article was published on 2005-01-15. It has received 322 citations till now. The article focuses on the topics: Linear discriminant analysis & FERET database.read more
Citations
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Journal ArticleDOI
Linear discriminant analysis: A detailed tutorial
TL;DR: A solid intuition is built for what is LDA, and how LDA works, thus enabling readers of all levels to get a better understanding of the LDA and to know how to apply this technique in different applications.
Journal ArticleDOI
Heterogeneous Face Recognition Using Kernel Prototype Similarities
Brendan Klare,Anil K. Jain +1 more
TL;DR: A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images, and Random sampling is introduced into the H FR framework to better handle challenges arising from the small sample size problem.
Journal ArticleDOI
Matching Forensic Sketches to Mug Shot Photos
TL;DR: Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images and leads to state-of-the-art accuracys when matching viewed sketches.
Journal ArticleDOI
Eigenfeature Regularization and Extraction in Face Recognition
TL;DR: Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR, and GT databases show that the method consistently outperforms others.
Journal ArticleDOI
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
Haiping Lu,How-Lung Eng,Cuntai Guan,Konstantinos N. Plataniotis,Anastasios N. Venetsanopoulos +4 more
TL;DR: A regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias.
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.
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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.
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Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Journal ArticleDOI
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.