Journal ArticleDOI
Asymmetric Principal Component and Discriminant Analyses for Pattern Classification
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TLDR
An asymmetric principal component analysis (APCA) is proposed to remove the unreliable dimensions more effectively than the conventional PCA to facilitate a reliable and discriminative feature extraction for the asymmetric classes and/or the unbalanced training data.Abstract:
This paper studies the roles of the principal component and discriminant analyses in the pattern classification and explores their problems with the asymmetric classes and/or the unbalanced training data. An asymmetric principal component analysis (APCA) is proposed to remove the unreliable dimensions more effectively than the conventional PCA. Targeted at the two-class problem, an asymmetric discriminant analysis in the APCA subspace is proposed to regularize the eigenvalue that is, in general, a biased estimate of the variance in the corresponding dimension. These efforts facilitate a reliable and discriminative feature extraction for the asymmetric classes and/or the unbalanced training data. The proposed approach is validated in the experiments by comparing it with the related methods. It consistently achieves the highest classification accuracy among all tested methods in the experiments.read more
Citations
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Journal ArticleDOI
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches
Chih-Fong Tsai,Yu-Chieh Hsiao +1 more
TL;DR: Three well-known feature selection methods, which are Principal Component Analysis (PCA), Genetic Algorithms (GA) and decision trees (CART), are used and the back-propagation neural network is developed for the prediction model.
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Human detection from images and videos
TL;DR: A comprehensive survey on the recent development and challenges of human detection in the thread of human object descriptors is provided, providing a thorough analysis of the state-of-the-art human detection methods and a guide to the selection of appropriate methods in practical applications.
Journal ArticleDOI
Unsupervised Domain Adaptation for Face Anti-Spoofing
TL;DR: This work introduces an unsupervised domain adaptation face anti-spoofing scheme to address the real-world scenario that learns the classifier for the target domain based on training samples in a different source domain, and introduces a new database for face spoofing detection.
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Linear Subspace Learning-Based Dimensionality Reduction
TL;DR: The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate.
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A collaborative representation based projections method for feature extraction
TL;DR: Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that CRP works well in feature extraction and leads to a good recognition performance.
References
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Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
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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.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
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.
Journal ArticleDOI
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.