3D face recognition with local shape descriptors

İnan, Tolga
This thesis represents two approaches for three dimensional face recognition. In the first approach, a generic face model is fitted to human face. Local shape descriptors are located on the nodes of generic model mesh. Discriminative local shape descriptors on the nodes are selected and fed as input into the face recognition system. In the second approach, local shape descriptors which are uniformly distributed across the face are calculated. Among the calculated shape descriptors that are discriminative for recognition process are selected and used for three dimensional face recognition. Both approaches are tested with widely accepted FRGCv2.0 database and experiment protocol. Reported results are better than the state-of-theart systems. Recognition performances for neutral and non-neutral faces are also reported.


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In this thesis, a novel 3D indirect shape analysis method is presented which successfully retrieves 3D shapes based on the hand-object interaction. In the first part of the study, the human hand information is processed and trans- ferred to the virtual environment by Leap Motion Controller. Position and rotation of the hand, the angle of the finger joints are used for this part in our method. Also, in this approach, a new type of feature, which we call inter- action point, is introduced. These interaction p...
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3D Face Reconstruction Using Stereo Images and Structured Light
OZTURK, Ahmet Oguz; Halıcı, Uğur; ULUSOY PARNAS, İLKAY; AKAGUNDUZ, Erdem (2008-04-22)
In this paper, the 3D face scanner that we developed using stereo cameras and structured light together is presented. Structured light having a pattern of vertical lines is used to create feature points and to match them easily. 3D point cloud obtained by stereo analysis is post processed to obtain the 3D model in obj format.
A comparison of subspace based face recognition methods
Gönder, Özkan; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2004)
Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution,...
Citation Formats
T. İnan, “3D face recognition with local shape descriptors,” Ph.D. - Doctoral Program, Middle East Technical University, 2011.