Book ChapterDOI
Intelligent Biometric Information Fusion using Support Vector Machine
Richa Singh,Mayank Vatsa,Afzel Noore +2 more
- pp 325-349
About:
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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Journal Article
Human centric approach to inhomogenious geospatial data fusion and actualization
TL;DR: Analysis of human eyemovements (driven by conscious and subconscious brain processes) while perceiving an inhomogeneous stereo dataset -provides a unique opportunity for the human computer symbiosed geospatial systems.
Dissertation
Application-driven Advances in Multi-biometric Fusion
TL;DR: A novel performance anchored score normalization technique is presented that aligns certain performance-related score values in the fused biometric sources leading to more accurate multi-biometric decisions when compared to conventional normalization approaches.
Proceedings ArticleDOI
A feature information based approach for enhancing score-level fusion in multi-sample biometric systems
TL;DR: Application of information content to score level fusion can increase the performance of a fusion algorithm and hence make it more robust to errors and can be applied to other systems involving the multiple biometric samples or scans.
Proceedings ArticleDOI
A video database for intelligent video authentication
TL;DR: A unique video database which consists of the real life moments of people and objects, captured under various illumination conditions and camera positions, subjected to various tampering attacks is described.
Book ChapterDOI
Neighbor Distance Ratios and Dynamic Weighting in Multi-biometric Fusion
TL;DR: The enhanced performance induced by including the neighbors distance ratio information within a dynamic weighting scheme in comparison to the baseline solution was shown by an average reduction of the equal error rate by more than 40% over the different test scenarios.
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?
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.
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.