Download or read book Statistical Computing on Manifolds for 3D Face Analysis and Recognition written by Hassen Drira and published by . This book was released on 2011 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic face recognition has many benefits over other biometric technologies due to the natural, non-intrusive, and high throughput nature of face data acquisition. Thus, the techniques for face recognition have received a growing attention within the computer vision community over the past three decades. In terms of a modality for face imaging, a major advantage of 3D scans over 2D color imaging is that variations in illumination and scaling have less influence on the 3D scans.However, scan data often suffer from the problem of missing parts dueto self-occlusions or imperfections in scanning technologies. Additionally, variations in face data due to facial expressions are challenging to 3D face recognition. In order to be useful in real-world applications, 3D face recognition approaches should be able to successfully recognize face scans even in the presence of large expression-based deformations and missing data due to occlusions and pose variation. Most recent research has been directed towards expression-invariant techniques and spent less effort to handle the missing parts problem. Few approaches handles the missing part problem but none has performed on a full database containing real missing data, they simulate some missing parts. We present a common framework handling both large expressions and missing parts due to large pose variation. In addition, with the same framework, we are able to average surfaces and hierarchically organize databases to allow efficient searches. In presence of occlusion, we propose to delete and restore occluded parts. The surface is first represented by radial curves (emanating from the nose tip fo the 3D face). Then a base is built using PCA for each curve. Hence, the missing part of the curve can be restored by projecting the existing part of it on the base. PCA is applied on the tangent space of the mean curve as it is linear space. Once the occlusion was detected and removed, the occlusion challenge can be handled as a missing data problem. Hence, we apply the restoration framework and then apply our radial-curve-based 3D face recognition algorithm.