A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from shading
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
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
Recovering Facial Shape Using a Statistical Model of Surface Normal Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using cartesian models of faces with a data-driven and integrable fitting framework
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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In previous work [5] we have identified the gradient of the surface as the best representation for constructing Cartesian models of faces. This representation proved capable of capturing variations in facial shape over a sample of training data. The resulting statistical model can be fitted to Lambertian data using a simple nonexhaustive parameter adjustment procedure. In this paper we test the ability of the surface gradient-based model in two directions. First, we deal with non-lambertian images. Second, we use the model for face recognition purposes. Experiments with real world images suggest that the surface gradient model with the proposed parameter search can be used for accurate face shape recovery, showing a potential for face recognition applications.