Bosphorus Database. 3D Face Database · Hand Database · 3D Face Database · 3D/2D Database of FACS annotated facial expressions, of head poses and of. The Bosphorus Database is a database of 3D faces which includes a rich set of IEEE CVPR’10 Workshop on Human Communicative Behavior Analysis, San. Bosphorus Database for 3D Face Analysis Arman Savran1, Neşe Alyüz2, Hamdi Dibeklioğlu2, Oya Çeliktutan1, Berk Gökberk3, Bülent Sankur1, Lale Akarun2 1.

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Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variation or when occlusion is present. This has also been enabled by the wider availability of 3D range scanners.

Clement Creusot, PhD

By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions. Second, since no video acquisition was possible for this database, the AUs were captured at their peak intensity levels, which were judged subjectively. Total Expression Pose Occl.

The desiderata of a 3D face database enabling a range of facial analysis tasks ranging from expression understanding to 3D recognition are the following: Local-descriptor map computation 1. During acquisition of each action unit, subjects were given explications about these expressions and they were given feedback if they did not enact correctly.


Most of them are focused on recognition; hence contain a limited range of expressions and head poses. For facial analysis and synthesis applications, non-rigid registration of faces is a very important intermediate step.

Bosphorus Database for 3D Face Analysis | Berk Gokberk and Nese Alyuz –

Data on hair and facial hair, such as beard and eyebrows, generally causes spiky noise. Scanner software is used for acquisition and 3D model reconstruction.

For each landmark, the proportion of face meshes that have an associated keypoint detection is used as a performance indicator. Top row shows basic filtering and self-occlusion problem. Finally conclusion is given in Section 5. My work was to evaluate new techniques for automatic face landmarking and face recognition. For each pixel i,j the z i,j coordinate is interpolated on the triangle intersected by the vector passing by x i,j ,y i,j and colinear to axis z.

All the programs provided here are under GPL v3except if specified otherwise in the headers.

Manually labeled 24 facial landmark points. Theory, Applications, and Systems, Therefore, the newly emerging goal in this field is to develop algorithms working with natural and uncontrolled behaviour of subjects.

anapysis Although various angles of poses were acquired, they are only approximations. It should be good enough as initial transformation but may require refinement for specific applications.

Database Content The database consists of 81 subjects in various poses, expressions and occlusion conditions.

Clement Creusot’s Home Page

LIC and generic solve. Compute a field as a function of different other fields mean: Since data are captured from single views with this system, self- occlusions occur.

Most of the existing methods for facial feature detection and person recognition assume frontal and neutral views only, and hence biometry systems have been adapted accordingly.


In the following subsections, the collected facial expressions, head poses and occlusions are explained. Non-rigid registration is quite an ill-posed problem and needs further attention. Head poses and occlusions in the Bosphorus database.

Second, for the eyeglass occlusion, subjects used different eyeglasses from a pool. I passed my viva in March Metin Sezgin from University of Cambridge.

In the middle row, noise due to hair, movement, and facial hair is seen. This database is unique from three aspects: This does not only affect the texture image of the face but can also cause noise in the 3D data.

Compute coarce tetrahedron volume in a given neighborhood willmore: Capture of the mesh. In this paper, we present an automatic method to detect keypoints on 3D faces, where these keypoints bosphprus locally similar to a set of previously learnt shapes, constituting a ‘local shape dictionary’.

Face recognition experiments have already been carried out on this database. Bibtex File [bib] Plain text. More convenient than a 3f equivalent: An example is shown at the middle row of Fig. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes.