This work investigates the use of a point distribution model to detect prominent features in a face (eyes, brows, mouth, etc) and the subsequent facial feature extraction and facial expression classification into seven categories (anger, fear, surprise, happiness, disgust, neutral and sadness). A multi-scale and multi-orientation Gabor filter bank, designed in such a way so as to avoid redundant information, is used to extract facial features at selected locations of the prominent features of a face (fiducial points). A region based approach is employed at the location of the fiducial points using different region sizes to allow some degree of flexibility and avoid artefacts due to incorrect automatic discovery of these points. A feed forward back propagation Artificial Neural Network is employed to classify the extracted feature vectors. The methodology is evaluated by forming 7 different regions and the feature vector is extracted at the location of 20 fiducial points.
['Anastasios C. Koutlas', 'Dimitrios I. Fotiadis']
An automatic region based methodology for facial expression recognition