Auto face re-ranking by mining the web and video archives

It is necessary to utilize visual information to improve the efficiency of retrieval in image-search engines that use textual information for indexing. One popular approach has been to learn visual consistency between images returned by these search engines. Most state-of-the-art methods of learning visual consistency usually learn one specific classifier for each query to re-rank the returned images. The main drawback with these query-specific based methods is that they require computational cost and processing time that are unsuitable for handling a large number of queries. Another approach has been to learn one generic classifier once and then use for all queries. Pursuing the generic classifier based approach, we study the problem of re-ranking faces returned by existing search engines to improve retrieval performance. Learning a generic classifier involves finding good query-dependent feature representation and collecting sufficient large number of training samples. Existing work [9, 15] studies query-dependent features for general objects rather than faces. In addition, training samples are usually collected manually. The key contribution of this research is to introduce a query-dependent feature for faces and an unsupervised method of automatically collecting training samples to learn the generic classifier. The experimental results demonstrated that the proposed method performed very well in various datasets.