A Survey on Human Emotion Recognition Approaches, Databases and Applications

This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed.This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed

Intelligent Facial Action and emotion recognition for humanoid robots

This research focuses on the development of a realtime intelligent facial emotion recognition system for a humanoid robot. In our system, Facial Action Coding System is used to guide the automatic analysis of emotional facial behaviours. The work includes both an upper and a lower facial Action Units (AU) analyser. The upper facial analyser is able to recognise six AUs including Inner and Outer Brow Raiser, Upper Lid Raiser etc, while the lower facial analyser is able to detect eleven AUs including Upper Lip Raiser, Lip Corner Puller, Chin Raiser, etc. Both of the upper and lower analysers are implemented using feedforward Neural Networks (NN). The work also further decodes six basic emotions from the recognised AUs. Two types of facial emotion recognisers are implemented, NN-based and multi-class Support Vector Machine (SVM) based. The NN-based facial emotion recogniser with the above recognised AUs as inputs performs robustly and efficiently. The Multi-class SVM with the radial basis function kernel enables the robot to outperform the NN-based emotion recogniser in real-time posed facial emotion detection tasks for diverse testing subjects