Real time emotions recognition through facial expressions

Human behavior is deeply influenced by emotions. Detection of emotions plays a pivotal role in understanding how individuals respond to various stimuli, such as reading text, encompassing feelings of anger, anxiety, confusion, or nervousness. Real-time facial emotion detection during online text reading represents an innovative approach for receiving immediate feedback based on readers' emotional responses. Real-time emotion detection finds applications in interactive displays and holds immense potential for online learning platforms, where it can be utilized to analyze students' emotional states and gauge their level of comprehension. Despite vast existing literature on emotion detection, real-time emotion detection is not very well studied. This study demonstrates the design and implementation of face emotion detection for students while they are using online learning platforms. The primary objective is capturing human emotions and storing them in the database after five seconds while they are reading online text. The system is implemented using SSD based on VB.NetV1. The proposed system has strong relevance for integration with online web applications to detect learners' real-time emotions. Experiments are performed using CK+ and JAFFE face datasets and results show 96.46% and 98.43% accuracy, respectively. The system not only provides accurate results but also enables high-quality, robust, and real-time feedback based on the facial expressions of readers, facilitating a deeper understanding of students' emotional engagement during their online learning experiences.