A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Adaptive threshold estimation via extreme value theory
IEEE Transactions on Signal Processing
Score normalization in multimodal biometric systems
Pattern Recognition
Models of large population recognition performance
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
How do correlation and variance of base-experts affect fusion in biometric authentication tasks?
IEEE Transactions on Signal Processing
Incorporating Model-Specific Score Distribution in Speaker Verification Systems
IEEE Transactions on Audio, Speech, and Language Processing
Reduced analytical dependency modeling for classifier fusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Journal of Visual Communication and Image Representation
Pattern Recognition Letters
Multimedia event detection with multimodal feature fusion and temporal concept localization
Machine Vision and Applications
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Recognition problems in computer vision often benefit from a fusion of different algorithms and/or sensors, with score level fusion being among the most widely used fusion approaches. Choosing an appropriate score normalization technique before fusion is a fundamentally difficult problem because of the disparate nature of the underlying distributions of scores for different sources of data. Further complications are introduced when one or more fusion inputs outright fail or have adversarial inputs, which we find in the fields of biometrics and forgery detection. Ideally a score normalization should be robust to model assumptions, modeling errors, and parameter estimation errors, as well as robust to algorithm failure. In this paper, we introduce the w-score, a new technique for robust recognition score normalization. We do not assume a match or non-match distribution, but instead suggest that the top scores of a recognition system's non-match scores follow the statistical Extreme Value Theory, and show how to use that to provide consistent robust normalization with a strong statistical basis.