Refactoring facial expressions: an automatic analysis of natural occurring facial expressions in iterative social dilemma

Many automatic facial expression recognizers now output individual facial action units (AUs), but several lines of evidence suggest that it is the combination of AUs that is psychologically meaningful: e.g., (a) constraints arising from facial morphology, (b) prior published evidence, (c) claims arising from basic emotion theory. We performed factor analysis on a large data set and recovered factors that have been discussed in the literature as psychologically meaningful. Further we show that some of these factors have external validity in that they predict participant behaviors in an iterated prisoner’s dilemma task and in fact with more precision than the individual AUs. These results both reinforce the validity of automatic recognition (as these factors would be expected from accurate AU detection) and suggest the benefits of using such factors for understanding these facial expressions as social signals

Conversational Agent: Developing a Model for Intelligent Agents with Transient Emotional States

The inclusion of human characteristics (i.e., emotions, personality) within an intelligent agent can often increase the effectiveness of information delivery and retrieval. Chat-bots offer a plethora of benefits within an eclectic range of disciplines (e.g., education, medicine, clinical and mental health). Hence, chatbots offer an effective way to observe, assess, and evaluate human communication patterns. Current research aims to develop a computational model for conversational agents with an emotional component to be applied to the army leadership training program that will allow for the examination of interpersonal skills in future research. Overall, the current research explores the application of the deep learning algorithm to the development of a generalized framework that will be based upon modeling empathetic conversation between an intelligent conversational agent (chatbot) and a human user in order to allow for higher level observation of interpersonal communication skills. Preliminary results demonstrate the promising potential of the seq2seq technique (e.g., through the use of Dialog Flow Chatbot platform) when applied to emotion-oriented conversational tasks. Both the classification and generative conversational modeling tasks demonstrate the promising potential of the current research for representing human to agent dialogue. However, this implementation may be extended by utilizing, a larger more high-quality dataset