Scalable attribute-driven face image retrieval

In recent years an explosion of online multimedia data has been witnessed. As an example, abundant photos recording every aspect of human life are available through social media. Among tremendous amount of photos, a significant fraction contains human faces. Faces are usually salient features of the photos. To understand and extract useful information from such gigantic data corpus, efficient and effective retrieval algorithms are demanded. Most face retrieval techniques rely on low-level image features to compare faces based on visual similarity. However, as humans we tend to simplify the recognition task by utilizing human attributes such as gender or race to help differentiate people on a higher semantic level. In this paper, we propose to use human attributes as high-level semantic cues to determine people׳s identities. To this end, we develop discriminative image features with attribute information encoded to achieve more accurate face image retrieval. To guarantee scalability, we propose using a binary coding scheme for the proposed attributed-based features. A re-ranking step after initial retrieval is incorporated to further improve the retrieval performance. We demonstrate the superiority of the proposed method compared to state-of-the-art on the LFW and Pubfig face datasets.