Facial Emotional Classifier For Natural Interaction

The recognition of emotional information is a key step toward giving computers the ability to interact more naturally and intelligently with people. We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of a set of characteristic facial points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles in the eyebrow and mouth shape). The method defines and detects the six basic emotions (plus the neutral one) in terms of this information and has been fine-tuned with a database of more than 1500 images. The system has been integrated in a 3D engine for managing virtual characters, allowing the exploration of new forms of natural interaction

The Sensitive Artificial Listner: an induction technique for generating emotionally coloured conversation

The aim of the paper is to document and share an induction technique (The Sensitive Artificial Listener) that generates data that can be both tractable and reasonably naturalistic. The technique focuses on conversation between a human and an agent that either is or appears to be a machine. It is designed to capture a broad spectrum of emotional states, expressed in ‘emotionally coloured discourse’ of the type likely to be displayed in everyday conversation. The technique is based on the observation that it is possible for two people to have a conversation in which one pays little or no attention to the meaning of what the other says, and chooses responses on the basis of superficial cues. In SAL, system responses take the form of a repertoire of stock phrases keyed to the emotional colouring of what the user says. The technique has been used to collect data of sufficient quantity and quality to train machine recognition systems

Measuring user emotionality on online videos: A comparison between self-report and facial expression analysis

One common factor that unites the popularity of online video viewers is their virality. Marketers and academics have been involved in the contemporary research not only to understand how online virality occurs but in addition how it can be measured. Thus, the aim of this paper is threefold: a) to advance the understanding of what online video virality is b) to propose a conceptual framework for measuring video virality c) to evaluate two main contrasting methods for measuring video virality. The conceptual framework identifies key elements to video virality as emotions and social groups, and the tools proposed to be used for measuring online video virality is the FaceReader and the online web questionnaire. The findings from the study indicate the existence of discriminant validity between the two methods which inherently adds to the theoretical advancement with the notion that video marketers or researchers cannot use self-report to measure emotions or use it synchronously with facial expression analysis on online videos

Exploring Emotion Representation to Support Dialogue in Police Training on Child Interviewing

Police officers when dealing with interviewing children have to cope with a complex set of emotions from a vulnerable witness. Triggers for recognising those emotions and how to build rapport are often the basis of learning exercises. However, current training pulls together the full complexity of emotions during role-playing which can be over-whelming and reduce appropriate learning focus. Interestingly a serious game’s interface can provide valuable training not because it represents full complex, multimedia interactions but because it can restrict emotional complexity and increase focus during the interactions on key factors for emotional recognition. The focus of this paper is to report on a specific aspect that was explored during the development of a serious game that aims to address the current police-training needs of child interviewing techniques, where the recognition of emotions plays an important role in understanding how to build rapport with children. The review of literature reveals that emotion recognition, through facial expressions, can contribute significantly to the perceived quality of communication. For this study an ‘emotions map’ was created and tested by 41 participants to be used in the development of a targeted interface design to support the different levels of emotion recognition. The emotions identified were validated with a 70 % agreement across experts and non-experts highlighting the innate role of emotion recognition. A discussion is made around the role of emotions and game-based systems to support their identification for work-based training. As part of the graphical development of the Child Interview Stimulator (CIS) we examined different levels of emotional recognition that can be used to support the in-game graphical representation of a child’s response during a police interview