基于深度概率图模型的鲁棒人脸画像合成

针对基于数据驱动的人脸画像合成算法像素特征缺乏对光照变化和复杂背景的鲁棒性,常合成低质量的画像的问题,文中提出基于深度概率图模型的鲁棒人脸画像合成算法.采用预处理方法调整测试照片的光照亮度和人脸姿态,使之与训练照片一致.采用深度特征代替像素特征进行近邻匹配,采用深度概率图模型对画像重建权重和深度特征权重联合建模,得到合成画像的最佳重构表示.为了提高画像合成速度,提出快速近邻搜索方法.实验验证文中算法的鲁棒性和快速性.

Robust Face Sketch Synthesis Based on Deep Probabilistic Graphical Models

Since pixel level features in the data-driven face sketch synthesis algorithms lack robustness to illumination variation and complex background, the quality of synthesized face sketches is poor. In this paper, a robust face sketch synthesis algorithm based on deep probabilistic graphical models is proposed. A preprocessing method is adopted to adjust illumination brightness and face pose of an input photo to make them consistent with the training photos. Instead of pixel feature, deep feature representation is utilized for neighbor selecting. A deep probabilistic graphical model is employed to jointly model the weight of sketch reconstruction and the weight of deep feature, and therefore the best reconstruction representation of the synthetic image is obtained. A fast nearest neighbor search method is proposed to speed up sketch synthesis. Experimental results verify robustness and rapidity of the proposed algorithm.