Predicting biometric system failure

Object recognition (or classification) systems largely emphasize improving system performance and focus on their "positive" recognition (or classification). Few papers have addressed the prediction of recognition algorithm failures, even though it directly addresses a very relevant issue and can be very important in overall system design. This is the first paper to focus on predicting the failure of a recognizer (or classifier) and verifying the correctness of the recognition (or classification) system This research provides a unique component to the overall understanding of biometric systems. The approach presented in the paper is the post-recognition analysis techniques (PRAT), where the similarity scores used in recognition are analyzed to predict the system failure or to verify the system correctness after a recognizer has been applied. Applying an AdaBoost learning the approach combines the features computed from the similarity measures to produce a patent pending system that predicts the failure of a biometric system. Because the approach is learning-based the PRAT is a general paradigm predicting failure of any "similarity-based" recognition (or classification) algorithm. Failure prediction, using a leading commercial face recognition system, is presented as an example to show how to use the approach. On outdoor weathered face data, the system demonstrated the ability to predict 90% of the underlying facial recognition system failures with a 15% false alarm rate