Expression flow for 3D-aware face component transfer
ACM SIGGRAPH 2011 papers
Asymmetric facial shape based on symmetry assumption
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Rapid 3D face reconstruction by fusion of SFS and Local Morphable Model
Journal of Visual Communication and Image Representation
Combination of physiological and behavioral biometric for human identification
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Static and dynamic 3D facial expression recognition: A comprehensive survey
Image and Vision Computing
Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis
Image and Vision Computing
Multi-view gait fusion for large scale human identification in surveillance videos
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Gait based human identity recognition from multi-view surveillance videos
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Pattern Recognition Letters
Making bas-reliefs from photographs of human faces
Computer-Aided Design
Multi-view multi-modal gait based human identity recognition from surveillance videos
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
3D facial expression synthesis from a single image using a model set
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Nearest neighbor weighted average customization for modeling faces
Machine Vision and Applications
Computer Vision and Image Understanding
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Human faces are remarkably similar in global properties, including size, aspect ratio, and location of main features, but can vary considerably in details across individuals, gender, race, or due to facial expression. We propose a novel method for 3D shape recovery of faces that exploits the similarity of faces. Our method obtains as input a single image and uses a mere single 3D reference model of a different person's face. Classical reconstruction methods from single images, i.e., shape-from-shading, require knowledge of the reflectance properties and lighting as well as depth values for boundary conditions. Recent methods circumvent these requirements by representing input faces as combinations (of hundreds) of stored 3D models. We propose instead to use the input image as a guide to "mold” a single reference model to reach a reconstruction of the sought 3D shape. Our method assumes Lambertian reflectance and uses harmonic representations of lighting. It has been tested on images taken under controlled viewing conditions as well as on uncontrolled images downloaded from the Internet, demonstrating its accuracy and robustness under a variety of imaging conditions and overcoming significant differences in shape between the input and reference individuals including differences in facial expressions, gender, and race.