Mining weakly labeled web facial images for search-based face annotation

• Main Authors: WANG, Dayang, HOI, Steven C. H., HE, Ying
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Auto face annotation; Web facial images; Unsupervised learning; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4175; https://ink.library.smu.edu.sg/context/sis_research/article/5178/viewcontent/MiningWeaklyLabeledWebFace_SIGIR_2011.pdf
• Institution: Singapore Management University

In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning tasks efficiently, and conduct an extensive empirical study on a web facial image database with 400 persons and 40,000 web facial images. Encouraging results showed that the proposed ULR technique can significantly boost the performance of the promising search-based face annotation scheme.