Gaussian Probabilistic Confidence Score for Biometric Applications
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.