Institutional Repository of Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
| Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions | |
Dong, Jun1,2,3,4 ; Yuan, Xue5; Xiong, Fanlun1
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| 2017-03-01 | |
| 发表期刊 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
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| 摘要 | In this paper, a novel facial-patch based recognition framework is proposed to deal with the problem of face recognition (FR) on the serious illumination condition. First, a novel lighting equilibrium distribution maps (LEDM) for illumination normalization is proposed. In LEDM, an image is analyzed in logarithm domain with wavelet transform, and the approximation coefficients of the image are mapped according to a reference-illumination map in order to normalize the distribution of illumination energy due to different lighting effects. Meanwhile, the detail coefficients are enhanced to achieve detail information emphasis. The LEDM is obtained by blurring the distances between the test image and the reference illumination map in the logarithm domain, which may express the entire distribution of illumination variations. Then, a facial-patch based framework and a credit degree based facial patches synthesizing algorithm are proposed. Each normalized face images is divided into several stacked patches. And, all patches are individually classified, then each patch from the test image casts a vote toward the parent image classification. A novel credit degree map is established based on the LEDM, which is deciding a credit degree for each facial patch. The main idea of credit degree map construction is the over-and under-illuminated regions should be assigned lower credit degree than well-illuminated regions. Finally, results are obtained by the credit degree based facial patches synthesizing. The proposed method provides state-of-the-art performance on three data sets that are widely used for testing FR under different illumination conditions: Extended Yale-B, CAS-PEAL-R1, and CMUPIE. Experimental results show that our FR frame outperforms several existing illumination compensation methods. |
| 文章类型 | Article |
| 关键词 | Face Recognition Lbp Context-grid Partitioning Single Training Image Invariable Facial Feature |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1142/S0218001417560031 |
| 关键词[WOS] | ILLUMINATION COMPENSATION ; VARYING ILLUMINATION ; LINEAR-SUBSPACES ; NORMALIZATION ; IMAGE ; REPRESENTATION ; MODELS |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | Natural Science Foundation of Jiangsu Province(BK20131090) ; Natural Science Foundation of Jiangsu Province(BK20131090) ; Natural Science Foundation of Jiangsu Province(BK20131090) ; Natural Science Foundation of Jiangsu Province(BK20131090) ; Six talent peaks project in Jiangsu Province(2011-wlw-005) ; Six talent peaks project in Jiangsu Province(2011-wlw-005) ; Six talent peaks project in Jiangsu Province(2011-wlw-005) ; Six talent peaks project in Jiangsu Province(2011-wlw-005) ; National Natural Science Foundation of China(61301186) ; National Natural Science Foundation of China(61301186) ; National Natural Science Foundation of China(61301186) ; National Natural Science Foundation of China(61301186) |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000395115500008 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.hfcas.ac.cn:8080/handle/334002/32892 |
| 专题 | 中科院合肥智能机械研究所 |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China 3.Wuxi Zhongke Intelligent Agr Dev Co Ltd, Wuxi 214000, Peoples R China 4.Jiangsu R&D Ctr Internet Things, Wuxi 214000, Peoples R China 5.Beijing Jiaotong Univ, Sch Elect & Informat Engn, 3 Shang Yuan Cun, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dong, Jun,Yuan, Xue,Xiong, Fanlun. Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2017,31(3):1-22. |
| APA | Dong, Jun,Yuan, Xue,&Xiong, Fanlun.(2017).Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,31(3),1-22. |
| MLA | Dong, Jun,et al."Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 31.3(2017):1-22. |
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