Optimisation of automatic face annotation system used within a collaborative framework 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 study, the authors first construct the personalised pyramid database units for each member 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. Next, they train and optimise the personalised multiple-kernel learning (MKL) classifier unit for each member, which utilises the MKL algorithm to locally adapt to each member, resulting in the production of high-quality face identification results for the current owner in the MKL face recognition module. Experimental results demonstrate that their proposed face annotation approach provides a substantially higher level of efficacy and efficiency than other face annotation approaches for real-life personal photographs with pose variations.