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@article{171292,
        author = {shravana and Ruchitha N and Swarna M and Twisha A},
        title = {Real-Time Emotion Recognition Using Raspberry Pi},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3836-3839},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171292},
        abstract = {Emotion recognition has become a critical area of research with applications in healthcare, security, and human-computer interaction. However, implementing real-time emotion recognition systems on low-cost and resource-constrained devices remains a significant challenge. This paper presents a lightweight, cost-effective solution for emotion recognition using a Raspberry Pi platform. The proposed system employs a camera module to capture facial images and utilizes deep learning techniques for emotion classification. A Convolutional Neural Network (CNN) model is trained on the FER2013 dataset and optimized for deployment on the Raspberry Pi. The system achieves real-time performance with minimal latency while maintaining competitive accuracy. Experimental results demonstrate the effectiveness of the model in recognizing six basic emotions: happiness, sadness, anger, surprise, fear, and neutrality. The lightweight design and portability make the system ideal for applications in remote monitoring and embedded systems. Future work will focus on improving accuracy with multi-modal inputs and expanding the system's adaptability to diverse environments.},
        keywords = {Emotion recognition, Raspberry pi, Deep learning, Convolutional Neural Networks, Computer vision},
        month = {December},
        }