Face Recognition based on a 3D Morphable Model gorithm is based on an analysis-by-synthesis technique that tional complexity of the fitting algorithm. 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. Download Citation on ResearchGate | Face recognition based on fitting a 3D morphable model | This paper presents a method for face.

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New citations to this author. Articles 1—20 Show more. This “Cited by” count includes citations to the following articles in Scholar. Estimating coloured 3D face models from single images: An analysis of maxillary anterior teeth: My profile My library Metrics Alerts. Automatic Face and Gesture Recognition, Each of our face models is created from a set of 3D face scans. IEEE Transactions on pattern analysis and machine intelligence 25 9, The model has two components: Starting from the average face in a frontal pose and in the center of the image, our fitting algorithm calculates for each model coefficient and for the imaging parameters, such as rotation angles, how they affect the difference between the synthetic image of the model, and the input image.

Professor of Computer Science, Universitaet Siegen. These coefficients describe the 3D shape and surface colors texturebased on the statistics observed in a dataset of examples.


New reecognition related to this author’s research. Recognition of Faces across changes in pose and illumination is one of the most challenging problems in Computer Vision. Their combined citations are counted only for the first article.

New articles by this author. Hence the appearance of a given face can be summarised by a set of coefficients that describe how much there is of each mode of variation.

Face Recognition and Modeling

If you would like to download and use any of the University of Surrey 3D face models, details of their availability are here. The number of modes of variation depends on the size of the mesh, and also is different for shape and texture.

Each vertex also has moel colour; hence the vertices define both the shape and the texture of a face. Human Vision and Electronic Imaging X, Each scan is in the form of a graph, where the vertices are locations on the surface of the face, and the edges connect the vertices to form a triangulated mesh.

Volker Blanz – Google Scholar Citations

What object attributes determine canonical views? International Futting on Artificial Neural Networks, We estimate the model pn by fitting the Morphable Model to the input images: Since 3D shape and texture are independent of viewing angle, the representation depends little on the specific imaging conditions.

The system can’t perform the operation now. The Journal of prosthetic dentistry 94 6, Then, all values are updated such that the image difference is reduced, until our model reproduces the color values found in the original image. Verified email at informatik. To what extent do unique parts influence recognition across changes in viewpoint? Get my own profile Cited by View all All Since Citations h-index 37 28 iindex 63 Computer Vision and Pattern Recognition Workshop, European Conference on Computer Vision, In order to identify a person, we compare the model coefficients with those of all individuals “known” to the system, and find the nearest neighbor.


The following articles are merged in Scholar. Given a single facial input image, a 3DMM can recover 3D face shape and texture and scene properties pose and illumination via a fitting process.

The development has taken place in several phases:. Each face is registered to a standard mesh, so that each vertex has the same location on any registered face. Email address for updates. Our approach uses the model coefficients of a 3D Morphable Model for ftiting the identity of a person.