A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Writer Identification: Statistical Analysis and Dichotomizer
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Improved Techniques for an Iris Recognition System with High Performance
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guide to Biometrics
Off-line Handwriting Identification Using HMM Based Recognizers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Using B-Spline Curves for Hand Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Iris Identification Using Wavelet Packets
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Biometric authentication: a machine learning approach
Biometric authentication: a machine learning approach
Models of large population recognition performance
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper concerns the generalizability of biometric identification results from small-sized closed systems to larger open systems. Many researchers have claimed high identification accuracies on closed system consisting of a few hundred or thousand members. Here, we consider what happens to these closed identification systems as they are opened to non-members. We claim that these systems do not generalize well as the non-member population increases. To support this claim, we present experimental results on writer and iris biometric databases using Support Vector Machine (SVM) and Nearest Neighbor (NN) classifiers. We find that system security (1-FAR) decreases rapidly for closed systems when they are tested in open-system mode as the number of non members tested increases. We also find that, although systems can be trained for greater closed-system security using SVM rather than NN classifiers, the NN classifiers are better for generalizing to open systems due to their superior capability of rejecting non-members.