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Modeling of Facial Wrinkles for Applications in Computer Vision

Chapter

Abstract

Analysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images, incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching.

Keywords

Facial Image Local Binary Pattern Active Appearance Model Marked Point Process Skin Texture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Center for Medical Image Science and Visualization (CMIV)Linköpings Universitet/USLinköpingSweden
  2. 2.Department of Electrical and Computer Engineering and the Center for Automation ResearchUMIACS, University of MarylandCollege ParkUSA

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