Sketch-based face recognition is an interesting task in vision and multimedia
research, yet it is quite challenging due to the great difference between face
photos and sketches. In this paper, we propose a novel approach for
photo-sketch generation, aiming to automatically transform face photos into
detail-preserving personal sketches. Unlike the traditional models synthesizing
sketches based on a dictionary of exemplars, we develop a fully convolutional
network to learn the end-to-end photo-sketch mapping. Our approach takes whole
face photos as inputs and directly generates the corresponding sketch images
with efficient inference and learning, in which the architecture are stacked by
only convolutional kernels of very small sizes. To well capture the person
identity during the photo-sketch transformation, we define our optimization
objective in the form of joint generative-discriminative minimization. In
particular, a discriminative regularization term is incorporated into the
photo-sketch generation, enhancing the discriminability of the generated person
sketches against other individuals. Extensive experiments on several standard
benchmarks suggest that our approach outperforms other state-of-the-art methods
in both photo-sketch generation and face sketch verification.

本研究提出了一种基于卷积网络的方法，实现将人脸照片转化为面部素描，并添加判别性正则化项提高生成的人物素描的可辨识度，该方法在多项基准测试中表现优于其他当前最先进方法。