Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
This addresses the challenge of identifying persons from incomplete sketches in security or forensic applications, but it is incremental as it builds on existing sketch less face image retrieval frameworks.
The paper tackles the problem of sketch-based face image retrieval when only a partial sketch is available, proposing a multi-granularity representation learning method that improves early retrieval performance, outperforming state-of-the-art baselines on two datasets.
In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to interact during the drawing process. Considering SLFIR problem, there is a large gap between a partial sketch with few strokes and any whole face photo, resulting in poor performance at the early stages. In this study, we propose a multigranularity (MG) representation learning (MGRL) method to address the SLFIR problem, in which we learn the representation of different granularity regions for a partial sketch, and then, by combining all MG regions of the sketches and images, the final distance was determined. In the experiments, our method outperformed state-of-the-art baselines in terms of early retrieval on two accessible datasets. Codes are available at https://github.com/ddw2AIGROUP2CQUPT/MGRL.