Multiple Representations-Based Face Sketch-Photo Synthesis | |
Peng, Chunlei1; Gao, Xinbo2; Wang, Nannan3; Tao, Dacheng4,5; Li, Xuelong6; Li, Jie1 | |
作者部门 | 光学影像学习与分析中心 |
2016-11-01 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
卷号 | 27期号:11页码:2201-2215 |
产权排序 | 6 |
摘要 | Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science. |
文章类型 | Article |
关键词 | Face Recognition Face Sketch-photo Synthesis Forensic Sketch Multiple Representations |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TNNLS.2015.2464681 |
收录类别 | SCI ; EI |
关键词[WOS] | LOCAL BINARY PATTERNS ; RECOGNITION ; IMAGE ; FEATURES ; CLASSIFICATION ; REGULARIZATION ; INFORMATION ; ENSEMBLE ; QUALITY ; SYSTEM |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61125204 ; Fundamental Research Funds for the Central Universities(JB149901 ; Program for Changjiang Scholars and Innovative Research Team in University of China(IRT13088) ; Shaanxi Innovative Research Team for Key Science and Technology(2012KCT-02) ; Chinese Academy of Sciences(KGZDEW-T03) ; Australian Research Council(FT-130101457 ; 61172146 ; XJS15049) ; DP-140102164 ; 61432014 ; LP-140100569) ; 61501339) |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000386940300005 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28566 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 3.Xidian Univ, Sch Telecommun Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 4.Univ Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 5.Univ Technol, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 6.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Peng, Chunlei,Gao, Xinbo,Wang, Nannan,et al. Multiple Representations-Based Face Sketch-Photo Synthesis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2016,27(11):2201-2215. |
APA | Peng, Chunlei,Gao, Xinbo,Wang, Nannan,Tao, Dacheng,Li, Xuelong,&Li, Jie.(2016).Multiple Representations-Based Face Sketch-Photo Synthesis.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,27(11),2201-2215. |
MLA | Peng, Chunlei,et al."Multiple Representations-Based Face Sketch-Photo Synthesis".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 27.11(2016):2201-2215. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Multiple Representat(4267KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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