Multimodal geometric sparse representation for reliable social network communication

In this work we plan to develop a framework called, Multimodal Geometric Sparse Representation (MGSR) for reliable social network communication with Face, and Fingerprint features of human individuals on social networks. Neighbor Flow Feature Extraction (NFFE) algorithm performs feature extraction using Neighborhood flow for both the face and fingerprint features. The most predominant attributes are stored in a geometric sparse vector for both the modalities that obtains the exact features even when integration is made for two different features, addressing, non-aligned features. Biometric Matching algorithm is designed to evaluate true positive rate of test data to the available training data. Experimental evaluation with Biometric Research Repositories conducted with varied model features and fusion template size show that the Biometric Template Matching algorithm is able to significantly improve true positive rate of human biometric samples. Further, we demonstrate that our NFFE framework has a low execution time suited for deployment in real time social network sites.