Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient representation for irradiance environment maps
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Selection of wavelet subbands using genetic algorithm for face recognition
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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3D Face modeling is still one of the biggest challenges in computer graphics. In this paper we present a novel framework that acquires the 3D shape, texture, pose and illumination of a face from a single photograph. Additionally, we show how we can recreate a face under varying illumination conditions. Or, essentially relight it. Using a custom-built face scanning system, we have collected 3D face scans and light reflection images of a large and diverse group of human subjects. We derive a morphable face model for 3D face shapes and accompanying textures by transforming the data into a linear vector sub-space. The acquired images of faces under variable illumination are then used to derive a bilinear illumination model that spans 3D face shape and illumination variations. Using both models we, in turn, propose a novel fitting framework that estimates the parameters of the morphable model given a single photograph. Our framework can deal with complex face reflectance and lighting environments in an efficient and robust manner. In the results section of our paper, we compare our methods to existing ones and demonstrate its efficacy in reconstructing 3D face models when provided with a single photograph. We also provide several examples of facial relighting (on 2D images) by performing adequate decomposition of the estimated illumination using our framework.