Retrieving Unfamiliar Faces: Towards Understanding Human Performance

Face image retrieval is to find from a dataset all images containing the same person in the query image. Automatic face retrieval has seen fast development in recent years, although humans still appear to be the better performer on this task. This paper reports a study towards understanding human performance on retrieving unfamiliar faces. Wild Web face images are utilized in the study, and two experiments are designed to assess human performance and behavior on the retrieval task. The experiments help to identify a set of important features and also to understand how human behaved when facing the task of retrieving unfamiliar faces. Such observations/conclusions may provide guidelines for improving existing automated algorithms.

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