Attribute Prototype Learning for Interactive Face Retrieval

Abstract

Interactive face retrieval aims at finding target subjects in face databases through human and machine interaction, which involves user feedback based on human perception and machine similarity measure in feature spaces. In this article, we propose an attribute prototype learning method to tackle the semantic gap between human and machine in face perception for fast interactive face retrieval. We reformulate the theoretical explanation of the interactive retrieval model and develop the algorithm of the heuristic solution of the model. Each module of the prototype model is learned with a set of identity-related facial attributes. The outputs of the prototype modules form the semantic representation. To adapt the prototype models across different databases, we propose a transfer selection algorithm based on the coherence measurements in interactive face retrieval. Coherence analysis proves that the proposed attribute prototype representation can effectively narrow down the semantic gap even in the case of cross-database transfer learning. The prototype representation can effectively reduce the feature dimension in the retrieval process. Real user retrieval with the Bayesian relevance feedback model shows that attribute prototype space is superior to low-level feature space and proves that interactive retrieval with attribute prototype representation can converge fast in large face databases.