Deep learning-based facial expression recognition for monitoring neurological disorders

Facial expressions play an important role in communication. Impaired facial expression is a common sign of numerous medical conditions, particularly neurological disorders. 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.