document: Lightweight automatic face annotation in media pages Labeling human faces in images contained in Web media stories enables enriching the user experience offered by media sites . We propose a lightweight framework for automatic image annotation that exploits named entities mentioned in the article to significantly boost the accuracy of face recognition . While previous works in the area labor to train comprehensive offline visual models for a pre-defined universe of candidates , our approach models the people mentioned in a given story on the y , using a standard Web image search engine as an image sampling mechanism . We overcome multiple sources of noise introduced by this ad-hoc process , to build a fast and robust end-to-end system from off-the-shelf error-prone text analysis and machine vision components . In experiments conducted on approximately 900 faces depicted in 500 stories from a major celebrity news website , we were able to correctly label 81.5 % of the faces while mislabeling 14.8 % of them .
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