A High-Efficiency and High-Accuracy Fully Automatic Collaborative Face Annotation System for Distributed Online Social Networks

The development of fully automatic face annotation techniques in online social networks is currently very important for effective management and organization of a large number of personal photos shared on social network platforms. In this paper, we first propose the personalized hierarchical database access architecture for each member by taking advantage of various social network context types to substantially reduce time consumption. Next, we construct the personalized and adaptive fused face recognition (FR) unit for each member, which uses the AdaBoost algorithm to fuse several different types of base classifiers to produce highly reliable face annotation results. Additionally, to efficiently select suitable personalized face recognizers and then effectively merge multiple personalized face recognizer results, we propose two collaborative FR strategies: the owner with a highest priority rule and using a weighted majority rule for query photos within our collaborative FR framework. The experiment results demonstrate that the evaluation methodologies produced F-measure and Similarity accuracy rates that were, respectively, 64.03% and 63.05% higher for the proposed method in comparison to other state-of-the-art face annotation methods, as well as demonstrating that our method can result in a reduction in overall processing time of 78.06%.