Real-time emotion recognition novel method for geometrical facial features extraction

Facial emotions provide an essential source of information commonly used in human communication. For humans, their recognition is automatic and is done exploiting the real-time variations of facial features. However, the replication of this natural process using computer vision systems is still a challenge, since automation and real-time system requirements are compromised in order to achieve an accurate emotion detection. In this work, we propose and validate a novel methodology for facial features extraction to automatically recognize facial emotions, achieving an accurate degree of detection. This methodology uses a real-time face tracker output to define and extract two new types of features: eccentricity and linear features. Then, the features are used to train a machine learning classifier. As result, we obtain a processing pipeline that allows classification of the six basic Ekman's emotions (plus Contemptuous and Neutral) in real-time, not requiring any manual intervention or prior information of facial traits.