Innovative Human Interaction System to Predict College Student Emotions Using the Extended MASK-R-CNN Algorithm - P, Dinesh; G, Thailambal (EAI Endorsed Transactions on Internet of Things, 2025)
There is a rising demand for emerging machines that can be self-decisive and intelligent. Machines can capture the emotions and gestures of college students to mechanise tasks and handle interactions better. Facial expressions based on emotion recognition are practices that play a substantial role in the modern fields of artificial intelligence and computer vision. Numerous manual methods for detecting emotions are focused on few basic emotions. Additionally, significant time is needed for appropriate detection. Nonetheless, these techniques are time-consuming and inefficient for obtaining better results. Therefore, an effective object detection model is needed to address such issues. To overcome these challenges, several studies have focused on object detection systems to provide effective emotion prediction. Conversely, it results in a lack of speed, precision and computational complexity. To improve object detection performance, the proposed model employs deep learning (DL)-based adaptive feature spatial anchor refinement with a mask region-based convolutional neural network (Mask RCNN). It uses the Facial Expression Recognition (FER) 2013 dataset for the evaluation process. Correspondingly, the efficacy of the projected model is calculated via various evaluation metrics, such as the recall, precision and mean average precision (mAP), to estimate the performance of the proposed DL method. It achieves 0.75298 for MAP@50, 0.70252 for precision and 0.66606 for recall. Furthermore, a comparison of existing models reveals the efficiency of the proposed DL method. The present research is intended to contribute to emerging object detection methods for enhancing real-time analysis of student emotions in various environments, such as classrooms and online education.
Keywords: Object detection; emotions prediction; Deep Learning; Mask RCNN; Facial expression recognition