Attribute-enhanced metric learning for face retrieval

Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval. Different from general cross-media retrieval, in the proposed model, the information of attribute labels are embedded in a hypergraph metric learning framework for face image retrieval tasks. The attribute labels serve to build a hypergraph, in which each image is abstracted as a vertex and is contained in several hyperedges. The learned hypergraph combines the attribute label to reform the topology of image similarity relationship. With the mined correlation among multiple facial attributes, the reformed metrics incorporates the semantic information in the general image similarity measure. We apply the metric learning strategy to both similarity face retrieval and interactive face retrieval. The proposed metric learning model effectively narrows down the semantic gap between human and machine face perception. The learned distance metric not only increases the precision of similarity retrieval but also speeds up the convergence distinctively in interactive face retrieval.