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Facial image retrieval plays a significant role in forensic investigationswhere an untrained witness tries to identify a suspect from a massive pool ofimages. However, due to the difficulties in describing human facial appearancesverbally and directly, people naturally tend to depict by referring towell-known existing images and comparing specific areas of faces with them andit is also challenging to provide complete comparison at each time. Therefore,we propose an end-to-end framework to retrieve facial images with relevancefeedback progressively provided by the witness, enabling an exploitation ofhistory information during multiple rounds and an interactive and iterativeapproach to retrieving the mental image. With no need of any extra annotations,our model can be applied at the cost of a little response effort. We experimenton \texttt{CelebA} and evaluate the performance by ranking percentile andachieve 99\% under the best setting. Since this topic remains little exploredto the best of our knowledge, we hope our work can serve as a stepping stonefor further research.

From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback