Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Handbook of Biometrics
Between Classification-Error Approximation and Weighted Least-Squares Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic neural classification
Neural Computation
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Score normalization in multimodal biometric systems
Pattern Recognition
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
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In design of a multibiometric system, a major concern is the learning cost in terms of computation complexity and memory usage due to large data size. In this paper, we propose an online learning network to circumvent the computational problem. Although conventional online learning algorithms can be adopted, their optimization of the fitting distance residuals does not meet the actual classification error requirement. A direct optimization to the classification performance is thus desired. Since the proposed classification-based formulation involves a class-specific weight which varies according to the total number of genuine-users and imposters, an online learning formulation becomes non-trivial. Extensive empirical evaluations on publicly available data sets show promising potential of the proposed method in terms of fusion verification accuracy and computational cost.