The 3DID face alignment system for verifying identity
Image and Vision Computing
Partial matching of interpose 3D facial data for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Automatic 3D facial region retrieval from multi-pose facial datasets
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
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Traditional 2D face recognition systems are not tolerant to changes in pose, lighting and expression. This dissertation explores the use of 3D data to improve face recognition by accounting for these variations. A two step, fully automatic, 3D surface alignment algorithm is developed to correlate the surfaces of two 3D face scans. In the first step, key anchor points such as the tip of the nose are used to coarsely align two face scans. In the second step, the Iterative Closest Point (ICP) algorithm is used to finely align the scans. The quality of the face alignment is studied in depth using a Surface Alignment Measure (SAM). The SAM is the root mean squared error over all the control points used in the ICP algorithm, after trimming to account for noise in the data. This alignment algorithm is fast (