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Recently, the development of automatic face annotation techniques in online social networks has become a promising research area for the purpose of management of the large numbers of photographs uploaded to social network platforms. In this paper, we construct the pyramid database for the current owner in the Pyramid Database Access Control module by effectively making use of various types of social network context to drastically reduce time expenditure and further boost the accuracy of face identification for real-life personal photo. In our experiments, our evaluation methodologies produced respective F-measure and Similarity accuracy values that were up to 35.40% and 37.57% higher for the proposed approach in comparison to other face annotation methods. Additionally, our efficiency results demonstrate that the proposed approach can produce a 87.44% reduction in the overall execution time. [ABSTRACT FROM PUBLISHER]

The development of fully automatic face annotation techniques in online social networks (OSNs) is currently very important for effective management and organization of the large numbers of personal photos shared on social network platforms. In this paper, we construct the personalized and adaptive Fused Face Recognition unit for each member, which uses the Adaboost algorithm to fuse several different types of base classifiers to produce highly reliable face annotation results. The experiment results demonstrate that our proposed approach achieves a significantly higher level of efficacy, outperforming other state-of-the-art face annotation methods for real-life personal photos featuring pose variations. Our evaluation methodologies produced respective F-measure and Similarity accuracy rates that were 57.99% and 54.23% higher for the proposed method in comparison to other tested methods. [ABSTRACT FROM PUBLISHER]