Identity-preserving face generation guided by Forensic Sketches
TL;DR
The proposed sketch-guided face generation pipeline based on a Conditional Variational Autoencoder designed to generate facial reconstructions from sketches and conditional attributes uses a stochastic preprocessing pipeline to extract edge maps from facial photographs, reducing dependence on manually paired sketch-photo datasets.
Abstract
Forensic sketching translates a witness description into a visual representation of a suspect, but sketches often lack visual details, which can limit their use in face recognition systems. This paper presents a sketch-guided face generation pipeline based on a Conditional Variational Autoencoder (CVAE) designed to generate facial reconstructions from sketches and conditional attributes. The method uses a stochastic preprocessing pipeline to extract edge maps from facial photographs, reducing dependence on manually paired sketch-photo datasets. Conditional inputs are incorporated to control ambiguous attributes that may not be fully specified by the sketch. The proposed approach is evaluated using synthetic edge maps, hand-drawn sketches, and digitally drawn sketches from different sources, considering both reconstruction quality and identity-oriented similarity. Compared with a vanilla autoencoder, the proposed CVAE reduced the FaceNet distance by 56.8% in the CUHK reconstruction evaluation. The experimental results also suggest that considering both reconstruction quality and identity preservation may provide a more complete evaluation of sketch-guided face generation in forensic scenarios.