ElderReact: A Multimodal Dataset for Recognizing Emotional Response in Aging Adults

Kaixin Ma, Xinyu Wang, Xinru Yang, Mingtong Zhang, Jeffrey M. Girard, Louis-Philippe Morency

Abstract: Automatic emotion recognition plays a critical role in technologies such as intelligent agents and social robots and is increasingly being deployed in applied settings such as education and healthcare. Most research to date has focused on recognizing the emotional expressions of young and middle-aged adults and, to a lesser extent, children and adolescents. Very few studies have examined automatic emotion recognition in older adults (i.e., elders), which represent a large and growing population worldwide. Given that aging causes many changes in facial shape and appearance and has been found to alter patterns of nonverbal behavior, there is strong reason to believe that automatic emotion recognition systems may need to be developed specifically (or augmented) for the elder population. To promote and support this type of research, we introduce a newly collected multimodal dataset of elders reacting to emotion elicitation stimuli. Specifically, it contains 1323 video clips of 46 unique individuals with human annotations of six discrete emotions: anger, disgust, fear, happiness, sadness, and surprise as well as valence. We present a detailed analysis of the most indicative features for each emotion. We also establish several baselines using unimodal and multimodal features on this dataset. Finally, we show that models trained on dataset of another age group do not generalize well on elders.