An efficient incremental face annotation for large scale web services

This paper proposes an incremental face annotation framework for sharing and publishing photographs which contain faces under a large scale web platform such as a social network service with millions of users. Unlike the conventional face recognition environment addressed by most existing works, the image databases being accessed by the large pool of users can be huge and frequently updated. A reasonable way to efficiently annotate such huge databases is to accommodate an adaptation of model parameters without the need to retrain the model all over again when new data arrives. In this work, we are particularly interested in the following issues related to increment of data: (i) the huge number of images being added at each instant, (ii) the large number of users joining the web each day, and (iii) the large number of classification systems being added at each period. We propose an efficient recursive estimation method to handle these data increment issues. Our experiments on several databases show that our proposed method achieves an almost constant execution time with comparable accuracy relative to those state-of-the-art incremental versions of principal component analysis, linear discriminant analysis and support vector machine.