Book ChapterDOI
Intelligent Biometric Information Fusion using Support Vector Machine
Richa Singh,Mayank Vatsa,Afzel Noore +2 more
- pp 325-349
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The article was published on 2007-01-01. It has received 27 citations till now. The article focuses on the topics: Support vector machine & Biometrics.read more
Citations
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An Overview on Multi-biometric Score-level Fusion - Verification and Identification
TL;DR: In this article, the authors present an overview of the multi-biometric score-level fusion problem, along with the proposed solution in the literature, and a discussion is made to provide a clearer view of future developments especially under the identification scenario where many related applications are rapidly growing.
Book ChapterDOI
Multi-biometric Score-Level Fusion and the Integration of the Neighbors Distance Ratio
Naser Damer,Alexander Opel +1 more
TL;DR: The novelty of this work focuses on integrating the relation of the fused scores to other comparisons within a 1:N comparison by considering the neighbors distance ratio in the ranked comparisons set within a classification-based fusion approach.
Proceedings ArticleDOI
Learning based video authentication using statistical local information
Saurabh Upadhyay,Sanjay Singh +1 more
TL;DR: An intelligent video authentication algorithm using support vector machine, which is a non-linear classifier, which computes the local information of the difference frames of given video statistically and classifies the video as tampered or non-tampered.
Proceedings Article
Multi-biometric continuous authentication: A trust model for an asynchronous system
TL;DR: This work presents a multi-biometric continuous authentication solution that includes information from the face images and the keystroke dynamics of the user, and designed to cope with the asynchronous nature induced by the different biometric characteristics.
Proceedings ArticleDOI
I-vector based physical task stress detection with different fusion strategies
TL;DR: A state-of-the-art ivector framework is investigated with MFCCs and the authors' previously formulated TEO-CB-Auto-Env features for neutral/physical task stress detection and two alternative fusion strategies (feature-level and score-level fusion) are investigated to validate this hypothesis.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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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
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
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Journal ArticleDOI
New Support Vector Algorithms
TL;DR: A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.