The role of image size in the recognition of conversational facial expressions Facial expressions can be used to direct the flow of a conversation as well as to improve the clarity of communication. The critical physical differences between expressions can, however, be small and subtle. Clear presentation of facial expressions in applied settings, then, would seem to require a large conversational agent. Given that visual displays are generally limited in size, the usage of a large conversational agent would reduce the amount of space available for the display of other information. Here, we examine the role of image size in the recognition of facial expressions. The results show that conversational facial expressions can be easily recognized at surprisingly small image sizes. Copyright xc2xa9 2004 John Wiley u0026 Sons, Ltd.

Accuracy improvement of facial expression recognition in speech acts: Confirmation of effectiveness of information around a mouth and GANbased data augmentation With the growth of the social robot market, much research has been undertaken on facial expression recognition, which is an important function of a social robot. Facial expression recognition models have shown good performance in a facial expression image dataset that expresses emotion without considering speaking effect. However, in reality, humans often express emotions by speaking and moving the muscles around the mouth. Therefore, the lack of consideration of speech leads to unsatisfactory emotion recognition results. In this paper, we investigated two points to be considered in learning a facial expression recognition model. First, we confirmed whether the information around a mouth induces the recognition model in speech act to misrecognition like the case of a facial expression recognition in nonspeech acts or it has valid information for facial expression recognition. Second, Generative Adversarial Network (GAN)based data augmentation has been performed to cover the problem in which the accuracy of the recognition model in speech acts is low because of the relatively small variance about the subject in RML dataset. The results showed that the information around the mouth made facial expression recognition in speech acts exhibit higher performance, unlike the case of facial expression recognition in nonspeech acts. In addition, the GANbased data augmentation alleviated the accuracy degradation in facial expression recognition because of the low variance of the dataset.