Detection of Occluded Face Image using Mean Based Weight Matrix and Support Vector Machine

Problem statement: Face occlusion is a very challenging problem in face recognition. The performance of face recognition system decreases drastically due to the presence of partial occlusion on the face. Extracting discriminative features to achieve accurate detection versus computational overhead in extracting the features, which affects the classification speed, would be a sustained problem. The objective of this study is to segment the human face into non-occluded and occluded part of the occluded human face image. In General, for face detection special facial features are extracted. In the proposed study a simplified algorithm to extract the features is developed. Approach: An algorithm which enables the automatic detection of the presence of occlusions on the face would be a useful tool to increase the performances of the system. The face image was preprocessed to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The experiment also balances both illumination and facial expression changes. Results: In this study, a Mean Based Weight Matrix (MBWM) algorithm has been proposed to enhance the performance by 4.25% than the LBP method. Conclusion: The proposed model has been tested on occluded face images with a dataset obtained from the MIT face database.