Abstract: Face detection has been around for ages. Taking a step forward, human emotion displayed by face and felt by brain, captured in either video, electric signal (EEG) or image form can be approximated. Emotion recognition is a critical aspect of human-computer interaction, enabling machines to understand and respond to human emotions effectively. This research focuses on the development and implementation of a real-time image processing system for emotion recognition. The objective is to create an efficient and accurate system capable of recognizing facial expressions in real- time, paving the way for applications in diverse fields such as human-computer interaction, healthcare, and entertainment.
The proposed system leverages advanced image processing techniques, including facial feature extraction, machine learning algorithms, and real-time data processing to analyze facial expressions accurately. A dataset comprising diverse facial expressions is used to train and validate the system, ensuring robust performance across a wide range of emotional states. The research also explores the integration of deep learning models, such as convolutional neural networks (CNNs), to enhance the system's ability to discern subtle nuances in facial expressions. To achieve real-time processing capabilities, parallel computing and optimization techniques are employed to streamline the computational workload. The system is designed to operate seamlessly on resource-constrained devices, making it applicable to a variety of platforms, including mobile devices and embedded systems
Index Terms: Real time Image, electric signal (EEG), convolutional neural networks (CNNs).
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DOI:
10.17148/IJARCCE.2024.13480
