基于深度概率图模型的鲁棒人脸画像合成

doi:10.16451/j.cnki.issn1003-6059.201909010

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Abstract 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.

Key words： Face Sketch Synthesis Robust Preprocessing Deep Probabilistic Graphical Model Fast Neighbor Search

Received: 15 May 2019

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61876142)

Corresponding Authors: WANG Nannan, Ph.D., professor. His research interests include computer vision, pattern recognition and machine learning.

About author:: ZHANG Yuqian, master student. Her research interests include computer vision, pa-ttern recognition and machine learning;GAO Fangyuan, undergraduate student.

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	ZHANG Yuqian
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Cite this article:

ZHANG Yuqian,GAO Fangyuan,WANG Nannan. Robust Face Sketch Synthesis Based on Deep Probabilistic Graphical Models[J]. , 2019, 32(9): 855-866.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201909010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I9/855

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