Expression Recognition Across Age
Abstract
Expression recognition is an important and growing field in AI. It has applications in fields including, but not limited to, medicine, security, and entertainment. A large portion of research, in this area, has focused on recognizing expressions of young and middle-age adults with less focus on children and elderly subjects. This focus can lead to unintentional bias across age, resulting in less accurate models. Considering this, we investigate the impact of age on expression recognition. To facilitate this investigation, we evaluate two state-of-the-art datasets, that focus on different age ranges (children and elderly), namely EmoReact and ElderReact. We propose a Siamese-network based approach that learns the semantic similarity of expressions relative to each age. We show that the proposed approach, to expression recognition, is able to generalize across age. We show the proposed approach is comparable to or outperforms current state-of-the-art on the EmoReact and ElderReact datasets.