We propose a quick and widely applicable approach for converting biometric identification match scores to probabilistic confidence scores, resulting in increased discrimination accuracy. This approach builds on a confidence scoring approach for Binomial distributions resulting from Hamming distances (commonly used in iris recognition). We derive a Gaussian confidence scoring approach that is three orders of magnitude faster than the Binomial approach while still resulting in higher recognition rates. Gaussian distributions are also more common and thus more widely applicable to different biometric systems. For probe-to-gallery (1-to-N) identification of the face recognition system tested, this approach has been shown to improve the identification rate from 25.66% to 68.05% at 1.00% false alarm rate for a CCTV video matching dataset, and from 63.34% to 73.14% for images from the LFW dataset. A sensitivity analysis demonstrates that modeling errors in genuine and impostor distributions only negatively impacts discrimination when the distribution means are modelled to be closer together than the true underlying distributions. For the reverse case where the distribution means are modeled to be further apart than the true distributions, discrimination accuracy is improved.
[1]
Marwan Mattar,et al.
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
,
2008
.
[2]
Brian C. Lovell,et al.
Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
,
2009,
ICB.
[3]
Alessandro Perina,et al.
Person re-identification by symmetry-driven accumulation of local features
,
2010,
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[4]
Yongkang Wong,et al.
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
,
2010,
2010 20th International Conference on Pattern Recognition.
[5]
Sidney Addelman,et al.
trans-Dimethanolbis(1,1,1-trifluoro-5,5-dimethylhexane-2,4-dionato)zinc(II)
,
2008,
Acta crystallographica. Section E, Structure reports online.
[6]
Dmitry O. Gorodnichy,et al.
Calibrated Confidence Scoring for Biometric Identification
,
2010
.
[7]
Bruce A. Draper,et al.
Overview of the Multiple Biometrics Grand Challenge
,
2009,
ICB.
[8]
Dmitry O. Gorodnichy,et al.
Score Calibration for Optimal Biometric Identification
,
2010,
Canadian Conference on AI.