Adaptive Quality-Based Performance Prediction and Boosting for Iris Authentication: Methodology and Its Illustration

Three practical methods to improve performance of a single biometric matcher based on vectors of quality measures associated with biometric data are described. The first two methods adaptively select probe biometric data and matching scores based on predicted values of Quality of Sample (QS) index (defined here as d-prime) and Confidence in matching Scores (CS), respectively. The third method, Quality Sample and Template features (QST), treats quality measures as weak but useful features for discriminating between genuine and imposter matching scores. The unifying theme for the three methods consists in learning a nonlinear mapping between vectors of quality measures and QS, CS, and QST for each of the three methods, respectively. For the first method, learning requires a small set of input data in the form of a vector of quality metrics per each biometric image and the output data in the form of QS estimated per image. For the second method, learning requires a small set of input data in the form of two vectors of quality metrics per each matching pair and the output data in the form of CS estimated per matching score. For the third method, learning requires a small set of input data in the form of biometric feature vector (template) concatenated with a vector of quality metrics and a set of output data in the form of matching labels. The proposed methodology is generic and is suitable for any biometric modality and for any choice of a nonlinear mapping between vectors of quality measures and QS, CS, and QST. The experimental results (obtained by means of neural nets) show significant performance improvements for all three methods when applied to iris biometrics.