Abstract

Facial expressions play an important role in com- munication. Impaired facial expression is a common sign of numerous medical conditions, particularly neurological disor- ders. Accurate automated systems are needed to recognize facial expressions and to reveal valuable information that can be used for diagnosis and monitoring of neurological disorders. This paper presents a novel deep learning approach for automatic facial expression recognition. The proposed architecture first segments the facial components known to be important for facial expression recognition and forms an iconized image; then performs facial expression classification using the obtained iconized facial components image combined with the raw facial images. This approach integrates local part-based features with holistic facial information for robust facial expression recognition. Preliminary experimental results using the proposed system achieved 93.43% facial expression recognition accuracy, more than 6% accuracy improvement compared to facial expression recognition from raw input images. The goal of the proposed study is design of a noninvasive, objective, and quantitative facial expression recognition system to assist diagnosis and monitoring of neurological disorders affecting facial expressions.