Multiparty authorization framework for data sharing in online social networks
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Retrieval-based face annotation by weak label regularized local coordinate coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Facing scalability: Naming faces in an online social network
Pattern Recognition
Incremental face recognition for large-scale social network services
Pattern Recognition
A unified learning framework for auto face annotation by mining web facial images
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to name faces: a multimodal learning scheme for search-based face annotation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.