[Invited papers] Interactive Face Retrieval Framework for Clarifying User's Visual Memory

Abstract

This paper presents an interactive face retrieval framework for clarifying an image representation envisioned by a user. Our system is designed for a situation in which the user wishes to find a person but has only visual memory of the person. We address a critical challenge of image retrieval across the user’s inputs. Instead of target-specific information, the user can select several images that are similar to an impression of the target person the user wishes to search for. Based on the user’s selection, our proposed system automatically updates a deep convolutional neural network. By interactively repeating these process, the system can reduce the gap between human-based similarities and computer-based similarities and estimate the target image representation. We ran user studies with 10 participants on a public database and confirmed that the proposed framework is effective for clarifying the image representation envisioned by the user easily and quickly.