Abstract: A method includes receiving probe image data associated with a biometric authentication request on a mobile device. A first classifier is employed to generate a first probability metric of the probe image data being associated with the authorized user. The biometric authentication request is approved responsive to the first probability metric being greater than a first threshold. The biometric authentication request is denied responsive to the first probability metric being less than a second threshold. Responsive to the probability metric being between the first and second thresholds, a second classifier is employed to generate a second probability metric of the probe image data being associated with the authorized user. The biometric authentication request is approved responsive to the second probability metric being greater than a third threshold.

Abstract: A method includes receiving probe image data associated with a biometric authentication request on a mobile device. A first classifier is employed to generate a first probability metric of the probe image data being associated with the authorized user. The biometric authentication request is approved responsive to the first probability metric being greater than a first threshold. The biometric authentication request is denied responsive to the first probability metric being less than a second threshold. Responsive to the probability metric being between the first and second thresholds, a second classifier is employed to generate a second probability metric of the probe image data being associated with the authorized user. The biometric authentication request is approved responsive to the second probability metric being greater than a third threshold.

Abstract: The present invention relates to pattern recognition and classification, more particularly, to a system and method for meta-recognition which can to predict success/failure for a variety of different recognition and classification applications. In the present invention, we define a new approach based on statistical extreme value theory and show its theoretical basis for predicting success/failure based on recognition or similarity scores. By fitting the tails of similarity or distance scores to an extreme value distribution, we are able to build a predictor that significantly outperforms random chance. The proposed system is effective for a variety of different recognition applications, including, but not limited to, face recognition, fingerprint recognition, object categorization and recognition, and content-based image retrieval system.