Ghaziasgar, MehrdadConnan, JamesMushfieldt, Diego2014-06-182024-10-302014-06-182024-10-302014https://hdl.handle.net/10566/16968>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 60◦. 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.enBlenderFace detectionFacial expression recognitionHaar featuresLocal binary patternsMorphological operationsOcclusionRotationSkin detectionSupport vector machineThesisUniversity of Western Cape