3D Face Representation Using Scale and Transform Invariant Features

In this study a representation using scale and invariant generic 3D features, for 3D facial models is proposed These generic feature vectors obtained from descriptive parts of the face like eyes, nose, or nose saddle, are then convolved into a graphical model where a characteristic topology for a 3D facial model representation is achieved These scale and invariant 3D features are determined by using the Gaussian (K) and Mean (H) curvature values on the facial surface and by examining various scales in the scale space. The curvatures are used to define fundamental elements on the surface such as, pits, peaks and saddles with their scale, normal and orientation information, where they assemble the mentioned generic features
IEEE 16th Signal Processing and Communications Applications Conference


3D face detection using transform invariant features
AKAGÜNDÜZ, erdem; Ulusoy, İlkay (2010-06-24)
A generic, transform invariant 3D facial feature detection method based on mean (H) and Gaussian (K) curvature analysis is proposed. A scale space of the HK values is constructed differently from the previous HK attempts. The 3D features are extracted from this scale space and used in a global topology, which is trained with a Gaussian model using only faces with neutral and frontal poses. The model is then tested against 1323 faces with various poses and expressions. The method is compared with four other ...
3D object representation using transform and scale invariant 3D features
AKAGÜNDÜZ, Erdem; Ulusoy, İlkay (2007-10-21)
An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a ...
3D face modeling using multiple images
3D face modeling based on real images is one of the important subject of Computer Vision that is studied recently. In this paper the study that eve contucted in our Computer Vision and Intelligent Systems Research Laboratory on 3D face model generation using uncalibrated multiple still images is explained.
3d face representation and recognition using spherical harmonics
Tunçer, Fahri; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2008)
In this study, a 3D face representation and recognition method based on spherical harmonics expansion is proposed. The input data to the method is range image of the face. This data is called 2.5 dimensional. Input faces are manually marked on the two eyes, nose and chin points. In two dimensions, using the marker points, the human face is modeled as two concentric half ellipses for the selection of region of interest. These marker points are also used in three dimensions to register the faces so that the n...
3D object recognition using scale space of curvatures
Akagündüz, Erdem; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2011)
In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches; connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric ...
Citation Formats
E. Akagündüz and İ. Ulusoy, “3D Face Representation Using Scale and Transform Invariant Features,” presented at the IEEE 16th Signal Processing and Communications Applications Conference, Aydın, Türkiye, 2008, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93678.