scispace - formally typeset
Search or ask a question
Author

Volker Blanz

Other affiliations: Max Planck Society, University of Freiburg, Bell Labs  ...read more
Bio: Volker Blanz is an academic researcher from University of Siegen. The author has contributed to research in topics: Face (geometry) & Facial recognition system. The author has an hindex of 42, co-authored 116 publications receiving 13196 citations. Previous affiliations of Volker Blanz include Max Planck Society & University of Freiburg.


Papers
More filters
Proceedings ArticleDOI
01 Jul 1999
TL;DR: A new technique for modeling textured 3D faces by transforming the shape and texture of the examples into a vector space representation, which regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance.
Abstract: In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

4,514 citations

Journal ArticleDOI
TL;DR: This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections, using computer graphics.
Abstract: This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.

2,187 citations

Journal ArticleDOI
TL;DR: It is found that exposure to an individual face for a few seconds generated a significant and precise bias in the subsequent perception of face identity, suggesting that the encoding of faces and other complex patterns draws upon contrastive neural mechanisms that reference the central tendency of the stimulus category.
Abstract: We used high-level configural aftereffects induced by adaptation to realistic faces to investigate visual representations underlying complex pattern perception. We found that exposure to an individual face for a few seconds generated a significant and precise bias in the subsequent perception of face identity. In the context of a computationally derived ‘face space,’ adaptation specifically shifted perception along a trajectory passing through the adapting and average faces, selectively facilitating recognition of a test face lying on this trajectory and impairing recognition of other faces. The results suggest that the encoding of faces and other complex patterns draws upon contrastive neural mechanisms that reference the central tendency of the stimulus category.

840 citations

Journal ArticleDOI
01 Sep 2003
TL;DR: A method for photo‐realistic animation that can be applied to any face shown in a single image or a video, which allows for head rotations and speech in the original sequence, but neither of these motions is required.
Abstract: This paper presents a method for photo-realistic animation of any face shown in a single image or a video. The technique does not require example data of the person’s mouth movements, and the image to be animated is not restricted in pose and illumination. Video reanimation allows for head rotations and speech in the original sequence, yet neither of these motions is required. In order to animate novel faces, the system transfers mouth movements and expressions across individuals, based a common representation of different identities and facial expressions in a vector space of 3D shapes and textures. This space is computed from 3D scans of different neutral faces, and scans of facial expressions. The 3D model’s versatility with respect to pose and illumination is conveyed to photo-realistic image and video processing by a framework of analysis and synthesis algorithms: The system automatically estimates 3D shape, pose and other rendering parameters from single images, and tracks head pose and mouth movements in video. Reanimated with new mouth movements, the 3D face is rendered into the original images.

448 citations

Book ChapterDOI
TL;DR: A 3D morphable model is used to compute 3D face models from three input images of each subject in the training database and the system achieved a recognition rate significantly better than a comparable global face recognition system.
Abstract: We present a novel approach to pose and illumination invariant face recognition that combines two recent advances in the computer vision field: component-based recognition and 3D morphable models. First, a 3D morphable model is used to generate 3D face models from three input images from each person in the training database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. These images are then used to train a component-based face recognition system. The resulting system achieved 90% accuracy on a database of 1200 real images of six people and significantly outperformed a comparable global face recognition system. The results show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition based on only three training images of each subject.

364 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

15,696 citations

Journal ArticleDOI
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

10,696 citations

Journal ArticleDOI
TL;DR: The meaning of the terms "method" and "method bias" are explored and whether method biases influence all measures equally are examined, and the evidence of the effects that method biases have on individual measures and on the covariation between different constructs is reviewed.
Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

8,719 citations

Journal ArticleDOI
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Abstract: A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

8,175 citations