Journal ArticleDOI
Improving kernel Fisher discriminant analysis for face recognition
TLDR
A new kernel function, called the cosine kernel, is proposed to increase the discriminating capability of the original polynomial kernel function and a geometry-based feature vector selection scheme is adopted to reduce the computational complexity of KFDA.Abstract:
This work is a continuation and extension of our previous research where kernel Fisher discriminant analysis (KFDA), a combination of the kernel trick with Fisher linear discriminant analysis (FLDA), was introduced to represent facial features for face recognition. This work makes three main contributions to further improving the performance of KFDA. First, a new kernel function, called the cosine kernel, is proposed to increase the discriminating capability of the original polynomial kernel function. Second, a geometry-based feature vector selection scheme is adopted to reduce the computational complexity of KFDA. Third, a variant of the nearest feature line classifier is employed to enhance the recognition performance further as it can produce virtual samples to make up for the shortage of training samples. Experiments have been carried out on a mixed database with 125 persons and 970 images and they demonstrate the effectiveness of the improvements.read more
Citations
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Journal ArticleDOI
A Survey of Face Recognition Techniques
Rabia Jafri,Hamid R. Arabnia +1 more
TL;DR: A discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has been provided.
Journal ArticleDOI
Multilinear Discriminant Analysis for Face Recognition
TL;DR: This paper presents a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order, and proposes a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction.
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.
Proceedings ArticleDOI
Learning Distance Metrics with Contextual Constraints for Image Retrieval
TL;DR: Experimental results show that the proposed algorithms for Discriminative Component Analysis and Kernel DCA are effective and promising in learning good quality distance metrics for image retrieval.
Journal ArticleDOI
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
TL;DR: The proposed algorithm is completely based on the HRV (R-R interval) signal which can be extracted from even a very noisy ECG signal with a relatively high accuracy and leads to an effective reduction of the processing time, which provides an online arrhythmia classification system.
References
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
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Eigenfaces for recognition
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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.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Journal ArticleDOI
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.