DEEP LEARNING BASED EMOTION DETECTION SYSTEM
CHETHAN R1 , VINAY PATEL G L2
STUDENT, DEPARTMENT OF MCA , BIET, DAVANGERE[1]
]ASSISTENT PROFESSOR , DEPARTMENT OF MCA, BIET, DAVANGERE[2]
ABSTRACT
Emotion detection plays a crucial role in human-computer interaction, affective computing, and psychological research. Traditional methods of emotion detection often rely on manual annotations or subjective assessments, which can be time-consuming and prone to biases. In this paper, we propose a Deep Learning-Based Emotion Detection System that leverages advanced neural network architectures to automatically recognize emotions from facial expressions in real-time. The system utilizes convolutional neural networks (CNNs) for feature extraction from facial images and recurrent neural networks (RNNs) for sequence modeling of temporal dynamics in facial expressions.
Our proposed system addresses key challenges in emotion detection, including variations in facial expressions, lighting conditions, and occlusions. By training on large-scale datasets of labeled facial images, the system learns to accurately classify emotions into predefined categories such as happiness, sadness, anger, fear, disgust, and surprise. We evaluate the performance of the system using standard metrics such as accuracy, precision, recall, and F1-score, demonstrating its effectiveness in real-world scenarios.
The results indicate that the Deep Learning-Based Emotion Detection System achieves state-of-the-art performance in recognizing emotions from facial expressions, outperforming traditional methods. The system's ability to analyze facial dynamics in real-time opens up opportunities for applications in human-computer interaction, virtual reality, mental health monitoring, and personalized user experiences. Keywords: Deep Learning, Emotion Detection, Facial Expression Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Affective Computing.
Keywords: emotion Detection, Machine Learning, Classifier, Natural Language Processing
