Automatic Face Annotation System Used Pyramid Database Architecture for Online Social Networks

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.