Multi-modal data fusion for person authentication based on improved ENN

People have paid more attention to biometrics based personal authentication recently. A single modality has its limitation in performance, such as universality and accuracy. The use of multiple modalities can get higher accuracy and winder universality. An improved ENN (Nearest-Neighbor with class Exemplars) method is proposed to fuse fingerprint and voiceprint. Compared with the traditional ENN and KNN (K-Nearest-Neighbor), the proposed method obtains further improvement of verification rates. The proposed method is also compared with the Bayesian fusion method. Performance of these two types of systems is given. Experimental results verify the validity of the proposed algorithm.