This article presents hierarchical fusion models for multi-biometric systems with improved recognition rate. Isolated texture regions are used to encode spatial variations from the composite biometric image which is generated by signal level fusion scheme. In this paper, the prominent issues of the existing multi-biometric system, namely, fusion methodology, storage complexity, reliability and template security are discussed. Here wavelet decomposition driven multi-resolution approach is used to generate the composite images. Texture feature metrics are extracted from multi-level texture regions and principal component analyzes are evaluated to select potentially useful texture values during template creation. Here through consistency and correlation driven hierarchical feature selection both inter-class similarity and intra-class variance problems can be solved. Finally, t-normalized feature level fusion is incorporated as a last stage to create the most reliable template for the identification process. To ensure the security and add robustness to spoof attacks random key driven permutations are used to encrypt the generated multi-biometric templates before storing it in a database. Our experimental results proved that the proposed hierarchical fusion and feature selection approach can embed fine detailed information about the input multi modal biometric images with the least complex identification process.
Texture Driven Hierarchical Fusion for Multi-Biometric Sys-tem
