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1. TIME-DELAY NEURAL NETWORK FOR CONTINUOUS EMOTIONAL DIMENSION
PREDICTION FROM FACIAL EXPRESSION SEQUENCES
ABSTRACT
Automatic continuous affective state prediction fromnaturalistic facial expression is a
very challenging research topicbut very important in human–computer interaction. One of
themain challenges is modeling the dynamics that characterize naturalistic expressions. In this
paper, a novel two-stage automaticsystem is proposed to continuously predict affective
dimensionvalues from facial expression videos. In the first stage, traditional regression methods
are used to classify each individualvideo frame, while in the second stage, a time-delay
neuralnetwork (TDNN) is proposed to model the temporal relationshipsbetween consecutive
predictions. The two-stage approachseparates the emotional state dynamics modeling from an
individualemotional state prediction step based on input features.In doing so, the temporal
information used by the TDNN isnot biased by the high variability between features of
consecutiveframes and allows the network to more easily exploitthe slow changing dynamics
between emotional states. The systemwas fully tested and evaluated on three different
facialexpression video datasets. Our experimental results demonstratethat the use of a two-stage
approach combined withthe TDNN to take into account previously classified framessignificantly
improves the overall performance of continuousemotional state estimation in naturalistic facial
expressions. The proposed approach has won the affect recognition sub-challengeof the Third
International Audio/Visual Emotion Recognition Challenger.