Multilevel Quadratic Variation Minimization for 3D Face Modeling and Virtual View Synthesis (original) (raw)

Finding Optimal Views for 3D Face Shape Modeling

2004

A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views (poses) required for accurate 3D shape estimation of a generic face. There is no analytical solution to this problem, instead (partial) solutions require (near) exhaustive combinatorial search, hence the inherent computational difficulty of this task. We build on our previous modeling framework which uses an efficient contour-based silhouette matching method and extend it by aggressive pruning of the view-sphere with view clustering and imposing various imaging constraints. A multiview optimization search is performed using both model-based (eigenhead) and datadriven (visual hull) methods, yielding comparable best views. These constitute the first reported set of optimal views for 3D face shape capture and should provide useful empirical guidelines for the design of 3D face recognition systems.

Automatic, Effective, and Efficient 3D Face Reconstruction from Arbitrary View Image

Lecture Notes in Computer Science, 2004

In this paper, we propose a fully automatic, effective and efficient framework for 3D face reconstruction based on a single face image in arbitrary view. First, a multi-view face alignment algorithm localizes the face feature points, and then EM algorithm is applied to derive the optimal 3D shape and position parameters. Moreover, the unit quaternion based pose representation is proposed for efficient 3D pose parameter optimization. Compared with other related works, this framework has the following advantages: 1) it is fully automatic, and only one single face image in arbitrary view is required; 2) EM algorithm and unit quaternion based pose representation are integrated for efficient shape and position parameters estimation; 3) the correspondence between 2D contour points and 3D model vertexes are dynamically determined by normal direction constraints, which facilitates the 3D reconstruction from arbitrary view image; 4) a weighted optimization strategy is applied for more robust parameter estimation. The experimental results show the effectiveness of our framework for 3D face reconstruction.

A state of art on 3D face modeling approaches

Electrical Engineering …, 2011

The model-based face recognition approach is based on constructing a model of the human face, which is able to capture the facial variations. The basic knowledge of human face is highly utilized to create the model. In this paper, we try to address and review the approaches and techniques used in the last ten years for modeling the human face in the 3D domain. Our discussion also shows the pros and cons of each approach used in the 3D face modeling.

Face Recognition with 3D Model-Based Synthesis

Lecture Notes in Computer Science, 2004

Current appearance-based face recognition system encounters the difficulty to recognize faces with appearance variations, while only a small number of training images are available. We present a scheme based on the analysis by synthesis framework. A 3D generic face model is aligned onto a given frontal face image. A number of synthetic face images are generated with appearance variations from the aligned 3D face model. These synthesized images are used to construct an affine subspace for each subject. Training and test images for each subject are represented in the same way in such a subspace. Face recognition is achieved by minimizing the distance between the subspace of a test subject and that of each subject in the database. Only a single face image of each subject is available for training in our experiments. Preliminary experimental results are promising.

Automatic 3D Reconstruction for Face Recognition

2004

An analysis-by-synthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace; Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) the proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant PIE.

An anthropometric face model using variational techniques

1998

We describe a system that automatically generates varied geometric models of human faces. A collection of random measurements of the face is generated according to anthropometric statistics for likely face measurements in a population. These measurements are then treated as constraints on a parameterized surface. Variational modeling is used to find a smooth surface that satisfies these constraints while using a prototype shape as a reference.

Chapter 2 A SURVEY ON 3D MODELING OF HUMAN FACES FOR FACE RECOGNITION

In its quest for more reliability and higher recognition rates the face recognition community has been focusing more and more on 3D based recognition. Depth information adds another dimension to facial features and provides ways to minimize the effects of pose and illumination variations for achieving greater recognition accuracy. This chapter reviews, therefore, the major techniques for 3D face modeling, the first step in any 3D assisted face recognition system. The reviewed techniques are laser range scans, 3D from structured light projection, stereo vision, morphing, shape from motion, shape from space carving, and shape from shading. Concepts, accuracy, feasibility, and limitations of these techniques and their effectiveness for 3D face recognition are discussed.