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Robust facial expression recognition in the presence of rotation and partial occlusion
>Magister Scientiae - MScThis research proposes an approach to recognizing facial expressions in the presence of
rotations and partial occlusions of the face. The research is in the context of automatic
machine translation of South African Sign Language (SASL) to English. The proposed
method is able to accurately recognize frontal facial images at an average accuracy of
75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60â—¦. It was
also shown that the method is able to continue to recognize facial expressions even in
the presence of full occlusions of the eyes, mouth and left/right sides of the face. The
accuracy was as high as 70% for occlusion of some areas. An additional finding was that
both the left and the right sides of the face are required for recognition. As an addition,
the foundation was laid for a fully automatic facial expression recognition system that
can accurately segment frontal or rotated faces in a video sequence
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
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