This paper presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. The approach of biometric image fusion at sensor level refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce a new fused image in spatially enhanced form. When the fused image is ready for further processing, SIFT operator are then used for feature extraction and the recognition is performed by adjustable structural graph matching between a pair of fused images by searching corresponding points using recursive descent tree traversal approach. The experimental result shows the efficacy of the proposed method with 98.19% accuracy, outperforms other methods when it is compared with uni-modal face and palmprint authentication results with recognition rates 89.04% and 92.17%, respectively and when all the methods are processed in the same feature space.
['Dakshina Ranjan Kisku', 'Jamuna Kanta Sing', 'Massimo Tistarelli', 'Phalguni Gupta']
Multisensor Biometric Evidence Fusion for Person Authentication Using Wavelet Decomposition and Monotonic-Decreasing Graph