• DocumentCode
    3094139
  • Title

    Local Regression Model for Automatic Face Sketch Generation

  • Author

    Ji, Naye ; Chai, Xiujuan ; Shan, Shiguang ; Chen, Xilin

  • Author_Institution
    Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing, China
  • fYear
    2011
  • fDate
    12-15 Aug. 2011
  • Firstpage
    412
  • Lastpage
    417
  • Abstract
    As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo, (2) our synthesized sketches presents more pencil sketch texture.
  • Keywords
    image processing; learning (artificial intelligence); regression analysis; automatic face sketch generation; face photo patch; face sketch patch; image patch level; learning-based strategy; local regression model; pencil sketch texture; photo-sketch; sketch portrait; Databases; Face; Hair; Law enforcement; Measurement; Rendering (computer graphics); Training; face sketch generation; k nearest neigbor; lasso; local regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Graphics (ICIG), 2011 Sixth International Conference on
  • Conference_Location
    Hefei, Anhui
  • Print_ISBN
    978-1-4577-1560-0
  • Electronic_ISBN
    978-0-7695-4541-7
  • Type

    conf

  • DOI
    10.1109/ICIG.2011.84
  • Filename
    6005598