Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin.
|Published - Jul 2018
|IEEE World Congress on Computational Intelligence 2018 - Windsor Barra Convention Centre, Rio de Janeiro, Brazil
Duration: 8 Jul 2018 → 13 Jul 2018
|IEEE World Congress on Computational Intelligence 2018
|Rio de Janeiro
|8/07/18 → 13/07/18