Conference Proceeding Article
Auto face annotation is an important technique for many real-world applications, such as online photo album management, new video summarization, and so on. It aims to automatically detect human faces from a photo image and further name the faces with the corresponding human names. Recently, mining web facial images on the internet has emerged as a promising paradigm towards auto face annotation. In this paper, we present a demonstration system of search-based face annotation: FANS - Face ANnotation by Searching large-scale web facial images. Given a query facial image for annotation, we first retrieve a short list of the most similar facial images from a web facial image database, and then annotate the query facial image by mining the top-ranking facial images and their corresponding labels with sparse representation techniques. Our demo system was built upon a large-scale real-world web facial image database with a total of 6,025 persons and about 1 million facial images. This paper demonstrates the potential of searching and mining web-scale weakly labeled facial images on the internet to tackle the challenging face annotation problem, and addresses some open problems for future exploration by researchers in web community. The live demo of FANS is available online at http://msm.cais.ntu.edu.sg/FANS/
Computer Sciences | Databases and Information Systems
Data Management and Analytics
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web: May 13-17, 2013, Rio de Janeiro, Brazil
International World Wide Web Conferences Steering Committee
City or Country
HOI, Steven; WANG, Dayong; CHENG, I Yeu; LIN, Elmer; ZHU, Jianke; HE, Ying; and MIAO, Chunyan.
FANS: Face Annotation by Searching Large-scale Web Facial Images. (2013). WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web: May 13-17, 2013, Rio de Janeiro, Brazil. 317-320. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2339
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