Face photo-line drawings synthesis based on local extraction preserving generative adversarial networks

Abstract

ABSTRACT Facial photo-to-sketch synthesis is crucial for entertainment and criminal investigations, yet challenges persist, including local detail blurring and identity feature loss. To mitigate these problems in face sketch synthesis, We propose a technique for synthesizing photographic sketches of faces using generative adversarial networks that focus on image localization to extract and preserve image identity features – namely LEPGAN. Our method introduces an image local extraction module, an attention mechanism in the generator, and an extended U-Net structure to enhance critical image features and preserve identity information. Multi-scale perceptual and local loss functions further enhance synthetic image quality. LEPGAN outperforms existing methods, producing images with crisper facial details that closely align with human visual perception, effectively preserving unique identity characteristics. Our approach represents a significant advancement in face sketch synthesis for various applications.