Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces

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