Indexing Multimodal Biometric Databases Using Kd-Tree with Feature Level Fusion

This paper proposes an efficient indexing technique that can be used in an identification system with large multimodal biometric databases. The proposed technique is based on Kd-tree with feature level fusion which uses the multi- dimensional feature vector. A multi dimensional feature vector of each trait is first normalized and then, it is projected to a lower dimensional feature space. The reduced dimensional feature vectors are fused at feature level and the fused feature vectors are used to index the database by forming Kd-tree. The proposed method reduces the data retrieval time along with possible error rates. The system is tested on multimodal databases (feature level fusion of ear; face, iris acid signature) consists of 5400 images of 150 subjects (i.e. 9 images per subject per trait). Out of the 9, 8 images are used for training and 1 is used for testing. The performance of the proposed indexing technique has been compared with indexing based on score level fusion. It is found that proposed technique based on feature level fusion performs better than score level fusion.