Abstract
The recognition of human emotion is a significant contribution to many computer vision appli-cations. Despite its importance, this work is the first one towards an automatic Autistic Children emotion recognition system to ensure their security during meltdown crisis. The current solutions to handle a meltdown crisis are based on a preventive approach. Indeed, Meltdown symptoms are determined by abnormal facial expressions related to compound emotions. To provide for this correspondence, we experimentally evaluate, in this paper, hand-crafted Geometric Spatio-Temporal and Deep features of realistic autistic children facial expressions. Towards this end, we compared the Compound Emotion Recognition (CER) performance for different combinations of these features, and we determined the features that best distinguish a Compound Emotion (CE) of autistic children during a meltdown crisis from the normal state. We used "Meltdown crisis"(1)dataset to conduct our experiments on realistic Meltdown / Normal scenarios of autistic children. In this evaluation, we show that the gathered features can lead to very encouraging performances through the use ofRandom Forestclassifier (91.27%) with hand-crafted features. Moreover, classifiers trained on deep features fromInceptionResnetV2show higher performance (97.5%) with supervised learning techniques.