CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Evaluating the Quality of Face Alignment without Ground Truth
Sheng, Kekai1; Dong, Weiming1; Kong, Yan1; Mei, Xing1; Li, Jilin2; Wang, Chengjie2; Huang, Feiyue2; Hu, Bao-Gang1
AbstractThe study of face alignment has been an area of intense research in computer vision, with its achievements widely used in computer graphics applications. The performance of various face alignment methods is often image-dependent or somewhat random because of their own strategy. This study aims to develop a method that can select an input image with good face alignment results from many results produced by a single method or multiple ones. The task is challenging because different face alignment results need to be evaluated without any ground truth. This study addresses this problem by designing a feasible feature extraction scheme to measure the quality of face alignment results. The feature is then used in various machine learning algorithms to rank different face alignment results. Our experiments show that our method is promising for ranking face alignment results and is able to pick good face alignment results, which can enhance the overall performance of a face alignment method with a random strategy. We demonstrate the usefulness of our ranking-enhanced face alignment algorithm in two practical applications: face cartoon stylization and digital face makeup.
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Science and Technology Major Program for Core Electronic Devices, High-end Generic Chips and Basic Software Project of China(2012ZX01039-004-45) ; National Natural Science Foundation of China(61172104 ; CASIA-Tencent BestImage joint research project ; 61271430 ; 61201402 ; 61372184)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Software Engineering
WOS IDWOS:000363216500021
Citation statistics
Document Type期刊论文
Corresponding AuthorDong, Weiming
Affiliation1.Chinese Acad Sci, Inst Automat, LIAMA NLPR, Beijing 100864, Peoples R China
2.Tencent, Shenzhen, Peoples R China
Recommended Citation
GB/T 7714
Sheng, Kekai,Dong, Weiming,Kong, Yan,et al. Evaluating the Quality of Face Alignment without Ground Truth[J]. COMPUTER GRAPHICS FORUM,2015,34(7):213-223.
APA Sheng, Kekai.,Dong, Weiming.,Kong, Yan.,Mei, Xing.,Li, Jilin.,...&Hu, Bao-Gang.(2015).Evaluating the Quality of Face Alignment without Ground Truth.COMPUTER GRAPHICS FORUM,34(7),213-223.
MLA Sheng, Kekai,et al."Evaluating the Quality of Face Alignment without Ground Truth".COMPUTER GRAPHICS FORUM 34.7(2015):213-223.
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