This dissertation studied the problem of face recognition when facial images have partial occlusions like sunglasses and scarfs. These partial occlusions lead to the loss of discriminatory information when trying to recognise a person's face using traditional face recognition techniques that do not take into account these shortcomings. This dissertation aimed to fill the gap of knowledge. Several papers in literature put forward the theory that not all regions of the face contribute equally when discriminating between different subjects. They state that some regions of the face are more equal than others, like the eyes and nose. While this may be true in theory there was a need to comprehensively study this problem. A weighting technique was introduced that that took into account the different features of the face and assigned weights for the different features of the face based on their distance from the five points that were identified as the centre of the weighing technique. Five centres were chosen which were the left eye, the right eye, the centre of the brows, the nose and the mouth. These centres perfectly captured were the five dominant regions of the face where roughly located. This weighing technique was fused with an image segmentation process that ultimately led to a hybrid approach to face recognition. Five features of the face were identified and studied quantitatively on how much they influence face recognition. These five features were the chin (C), eyes (E), forehead (F), mouth (M) and finally the nose (N). For the system to be robust and thorough, combinations of these five features were constructed to make 31 models that were used for both training and testing purposes. This meant that each of the five features had 16 models associated with it. For example, the chin (C) had the following models associated with it; C, CE, CF, CM, CN, CE, CEM, CEN, CFM, CFN, CMN, CEFM CEFN, CEMN, CFMN and CEFMN. These models were put in five different groupings called Category 1 up to Category 5. A Category 3 model implied that only three out of the five features were utilised for training the algorithm and testing. An example of a Category 3 model was the CFN model. This meant that this model simulated partial occlusion on the mouth and the chin region. The face recognition algorithm was trained on all these different models in order to ascertain the efficiency and effectiveness of this proposed technique. The results were then compared with various methods from the literature. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted