Multi-Scale Feature Channel Attention Generative Adversarial Network for Face Sketch Synthesis

Abstract

Face sketch synthesis for photos is an applied research topic and it is critical for criminal investigation. However, sketch synthesis remains some challenges because of the blur and artifacts in the generated face sketches. To mitigate these problems in face sketch synthesis, we propose a fast Generative Adversarial Network with fast Multi-scale feature channel Attention, namely MAGAN. In the generator network, multi-scale features are extracted by proposed multi-scale feature extraction to produce detailed sketches. Then, a channel attention mechanism is applied to emphasize the significance of important feature channels, further enhancing the synthesized sketches. Besides, the loss of patch-wise high-layer features from the VGG-19 network is applied to supervise the generator to synthesize more realistic sketches. To accelerate the training process, the features from the pooling layers are adopted to calculate the pseudo sketch feature loss. The experimental results demonstrate that our MAGAN can achieve better performance in both visual evaluations and quantitative evaluations (in terms of feature similarity and learned perceptual image patch similarity), compared with the state-of-the-art methods.