Abstrakt
Police photo lineups are an important part of criminal proceedings, where the task is to identify the perpetrator among photos of other persons (fillers). In order to prevent major errors in criminal proceedings, lineups should be unbiased (ie the suspect and fillers should share similar appearance characteristics).
Capability to assemble unbiased lineups is often hindered by the lack of effective methods to explore the database of fillers (ie good fillers are hard to be found), but also by the insufficient size of the database itself (ie no good fillers exist). In this demo, we present LiGAN application aiming on onthe-fly recommendation of artificial fillers for police photo lineups.
We consider this to be a highly novel recommending task, where items can be generated with arbitrary density and arbitrary precision to the (estimated) user's needs. LiGAN utilizes StyleGAN2 architecture to generate images, identity-preserving autoencoder for suspect seeding and optional model finetuning for individual lineups.
It recommends fillers based on the semantic proximity to the suspect, or as an interpolation between suspect and filler images. As such, LiGAN aims to contribute towards both the fillers existence and the fillers findability problems.