A 3D Face Model for Pose and Illumination Invariant Face Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
3D reconstruction of human faces from occluding contours
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Model-based stereo with occlusions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Exploring the identity manifold: constrained operations in face space
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Proceedings of the 32nd DAGM conference on Pattern recognition
Face recognition based on 2D images under illumination and pose variations
Pattern Recognition Letters
Driving 3D morphable models using shading cues
Pattern Recognition
3D morphable model parameter estimation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Model-based ambient occlusion for inverse rendering
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
State of the Art Report on Video-Based Graphics and Video Visualization
Computer Graphics Forum
Robust Bayesian fitting of 3D morphable model
Proceedings of the 10th European Conference on Visual Media Production
3D face sparse reconstruction based on local linear fitting
The Visual Computer: International Journal of Computer Graphics
Hi-index | 0.00 |
We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D Morphable Model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the Multi-Features Fitting algorithm that has a wider radius of convergence and a higher level of precision. This is shown on some example photographs, and on a recognition experiment performed on the CMU-PIE image database.