Automatic Pixel Selection for Optimizing Facial Expression Recognition using Eigenfaces

A new direction in improving modern dialogue systems is to make a human-machine dialogue more similar to a human-human dialogue. This can be done by adding more input modalities, e.g. facial expression recognition. A common problem in a human-machine dialogue where the angry face may give a clue is the recurrent misunderstanding of the user by the system. This paper describes recognizing facial expressions in frontal images using eigenspaces. For the classification of facial expressions, rather than using the whole image we classify regions which do not differ between subjects and at the same time are meaningful for facial expressions. Using this face mask for training and classification of joy and anger expressions of the face, we achieved an improvement of up to 11% absolute. The portability to other classification problems is shown by a gender classification.