IET CVI 3D SI 2008

Estimating 3D face shape from one or more images is a longstanding goal of computer vision. In the earliest work on shape-from-shading, researchers applied their algorithms to face images with little success. Advances during the last decade have seen the development of techniques that offer robust performance on real world images. Meanwhile, advances in structured light scanning and other non-standard sensing modalities have made high-end acquisition of 3D structure and motion a reality, albeit in controlled settings. A clear result to come from this work is that the processing of 3D face data requires techniques that span a number of fields. These include statistical shape modelling, non-linear optimisation, reflectance modelling, illumination estimation and shape-from-shading. These advances hold out the hope of estimating intrinsic properties of a face from single images or video streams. This is clearly attractive in the domain of face recognition where modelling appearance variation caused by large changes in pose, illumination and expression remains a key problem. Applications also lie in model acquisition for graphics applications, retouching faces in images (for example, adjusting expressions or illumination conditions) or even exchanging faces between images.

This Special Issue is associated with the workshop on "3D Face Processing" held in conjunction with the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) held in Anchorage, Alaska in June 2008. Therefore, contributors to the workshop are particularly invited to submit papers. However, contribution to this Special Issue is open to all researchers working in the field and they are strongly encouraged to make a submission.

Topics of interest include, but are not limited to, the following:

3D morphable face models 2D+3D active appearance models Face/skin reflectance modelling Facial shape-from-shading and photometric stereo Stereo for face images Psychological or neuropsychological investigations into the role 3D information plays in face processing in humans Structured light/Shape-from-X for face shape recovery Estimation of illumination or shadowing from images Modelling variation in appearance due to 3D shape using spherical harmonics, light fields etc Dynamic 3D face processing in video images, e.g. tracking, modelling of expressions in 3D, use of motion capture data Real-time 3D face scanning from video Colour information for 3D face processing Fusion of multimodal face information, e.g. 3D scans, high-speed video, high-resolution imaging Data management for large 3D face data sets Matching of partial or deformed scans

Applications of interest include: Facial shape estimation Recognition/classification using 3D information estimates from images Facial retouching, expression/texture transfer, relighting using 3D models Medical applications of 3D face modelling and facial expression analysis

Guest Editors: Prof. Volker Blanz, University of Siegen Dr. Baback Moghaddam, California Institute of Technology Prof. Hanspeter Pfister, Harvard School of Engineering Prof. Dimitris Samaras, Stony Brook University Dr. William Smith, University of York

Paper Format and Submission : Papers must be typed in a font size no smaller than 10 pt, and presented in single-column format with double line spacing on one side A4 paper. All pages should be numbered. The manuscript should be formatted according to the IET Proceedings requirements, typically 4000-6000 words long with 6-10 Figures. Detailed information about IET Research Journals, including an author guide and formatting information is available at: http://www.theiet.org/publications/journals/

All papers must be submitted through the journal's Manuscript Central system: http://mc.manuscriptcentral.com/iet-cvi When uploading your paper, please ensure that your manuscript is marked as being for this special issue.

Important Dates:

Submission: Sept 26, 2008 First Decision: Dec 31, 2008 Revised Manuscript: Mar 2009 Publication: June 2009 This CfP was obtained from WikiCFP