Abstract
Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.
Highlights
Automatic facial expression recognition and analysis has been an active topic in the scientific community for over two decades
315 sequences of the data set are selected from the database, for basic facial expression recognition
We achieved 95.17% of facial expression recognition accuracy using multi-class AdaBoost with dynamic time warping (DTW)
Summary
Automatic facial expression recognition and analysis has been an active topic in the scientific community for over two decades (refer to [1] for a recent review). Recent psychological research has shown that facial expressions are the most expressive way in which humans display emotion. The verbal part of the message contributes only 7% of the effect of the message as a whole, and the vocal part 38%, while facial expression contributes 55% of the effect of the speaker’s message [2]. Automated and real-time facial expression recognition would be useful in many applications, e.g., humancomputer interfaces, virtual reality, video-conferencing, customer satisfaction studies, etc. Several research efforts have been made regarding facial expression recognition. Facial expressions are divided by psychologists into six basic categories: anger, disgust, fear, happiness, sadness, and surprise [3]
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