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Biometric Recognition of 3D Faces
 Mráček, Štěpán ; Drahanský, Martin (oponent) ; Dvořák, Radim (vedoucí práce)
This Master's Thesis was performed during a study stay at the Gjovik University College, Norway. This Thesis is about biometric 3D face recognition. A general biometric system as well as specific techniques used in 2D and 3D face recognition are described. An automatic modular 3D face recognition method will be proposed. The algorithm is developed, tested and evaluated on the Face Recognition Grand Challenge (FRGC) database. During the preprocessing part, facial landmarks are located on the face surface and the three dimensional model is aligned to a predefined position. In the comparison module, the input probe scan is compared to the gallery template. There are three fundamental face recognition algorithms employed during the recognition pipeline -- the eigenface method (PCA), the recognition using histogram-based features, and the recognition based on the anatomical-Bertillon features of the face. Finally the decision module fuses the scores provided by the utilized recognition techniques. The resulting performance is better than any of utilized recognition algorithms.
Úplný záznam
Biometric Recognition of 3D Faces
 Mráček, Štěpán ; Drahanský, Martin (oponent) ; Dvořák, Radim (vedoucí práce)
This Master's Thesis was performed during a study stay at the Gjovik University College, Norway. This Thesis is about biometric 3D face recognition. A general biometric system as well as specific techniques used in 2D and 3D face recognition are described. An automatic modular 3D face recognition method will be proposed. The algorithm is developed, tested and evaluated on the Face Recognition Grand Challenge (FRGC) database. During the preprocessing part, facial landmarks are located on the face surface and the three dimensional model is aligned to a predefined position. In the comparison module, the input probe scan is compared to the gallery template. There are three fundamental face recognition algorithms employed during the recognition pipeline -- the eigenface method (PCA), the recognition using histogram-based features, and the recognition based on the anatomical-Bertillon features of the face. Finally the decision module fuses the scores provided by the utilized recognition techniques. The resulting performance is better than any of utilized recognition algorithms.
Úplný záznam

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