Abstract
The exemplar-based face sketch synthesis method generally contains two steps: neighbor
selection and reconstruction weight representation. Pixel intensities are widely used
as features by most of the existing exemplar-based methods, which lacks of representation
ability and robustness to light variations and clutter backgrounds. We present a novel
face sketch synthesis method combining generative exemplar-based method and discriminatively
trained deep convolutional neural networks (dCNNs) via a deep graphical feature learning
framework. Our method works in both two steps by using deep discriminative representations
derived from dCNNs. Instead of using it directly, we boost its representation capability
by a deep graphical feature learning framework. Finally, the optimal weights of deep
representations and optimal reconstruction weights for face sketch synthesis can be
obtained simultaneously. With the optimal reconstruction weights, we can synthesize
high quality sketches which is robust against light variations and clutter backgrounds.
Extensive experiments on public face sketch databases show that our method outperforms
state-of-the-art methods, in terms of both synthesis quality and recognition ability.
Keywords
- Machine Learning: Learning Graphical Models
- Machine Learning: Deep Learning
