Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences

Hongying Meng, Nadia Bianchi-Berthouze, Yangdong Deng, Jinkuang Cheng, John P. Cosmas
2016 IEEE Transactions on Cybernetics  
Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expression videos. In the first stage, traditional regression methods are used to classify each individual
more » ... ideo frame, while in the second stage, a Time-Delay Neural Network (TDNN) is proposed to model the temporal relationships between consecutive predictions. The two-stage approach separates the emotional state dynamics modeling from an individual emotional state prediction step based on input features. In doing so, the temporal information used by the TDNN is not biased by the high variability between features of consecutive frames and allows the network to more easily exploit the slow changing dynamics between emotional states. The system was fully tested and evaluated on three different facial expression video datasets. Our experimental results demonstrate that the use of a twostage approach combined with the TDNN to take into account previously classified frames significantly improves the overall performance of continuous emotional state estimation in naturalistic facial expressions. The proposed approach has won the affect recognition sub-challenge of the third international Audio/Visual Emotion Recognition Challenge (AVEC2013) 1 .
doi:10.1109/tcyb.2015.2418092 pmid:25910269 fatcat:wdqhwbsj6fcwjmqg7mgowyuabm