Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

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

Facial Landmark Based Region of Interest Localization for Deep Facial Expression Recognition

Automated facial expression recognition has gained much attention in the last years due to growing application areas such as computer animated agents, sociable robots and human computer interaction. The realization of a reliable facial expression recognition system through machine learning is still a challenging task particularly on databases with large number of images. Convolutional Neural Network (CNN) architectures have been proposed to deal with large numbers of training data for better accuracy. For CNNs, a task related best achieving architectural structure does not exist. In addition, the representation of the input image is equivalently important as the architectural structure and the training data. Therefore, this study focuses on the performances of various CNN architectures trained by different region of interests of the same input data. Experiments are performed on three distinct CNN architectures with three different crops of the same dataset. Results show that by appropriately localizing the facial region and selecting the correct CNN architecture it is possible to boost the recognition rate from 84% to 98% while decreasing the training time for proposed CNN architectures