Enhancing decision combination of face and fingerprint by exploitation of individual classifier space: An approach to multi-modal biometry

This paper presents a new approach to combine decisions from face and fingerprint classifiers for multi-modal biometry by exploiting the individual classifier space on the basis of availability of class-specific information present in the classifier space. We exploit the prior knowledge by training the face classifier using response vectors on a validation set, enhancing class separability (using parametric and nonparametric Linear Discriminant Analysis) in the classifier output space and thereby improving the performance of the face classifier. Fingerprint classifier often does not provide this information due to high sensitivity of available minutiae points, producing partial matches across subjects. The enhanced face and fingerprint classifiers are combined using a sum rule. We also propose a generalized algorithm for multiple classifier combination (MCC) based on our approach. Experimental results show superiority of the proposed method over other existing fusion techniques, such as sum, product, max, min rules, decision template and Dempster–Shafer theory.