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@article{168438,
        author = {Chaitali Gade and Bhagyashree satao and Mrunali Kitukale and Pratiksha Korde and Prof. Sandeep Ganorkar},
        title = {Facial Emotion Detection Using Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1048-1051},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168438},
        abstract = {Recognizing facial expressions is becoming increasingly important in areas such as human-computer interaction, mental health assessments, and security systems. However, accurately distinguishing between the six primary emotions - happiness, sadness, anger, surprise, disgust, and fear - presents significant challenges, especially in settings with changing lighting, different camera angles, and obstructions. This article presents an innovative approach to overcoming these challenges by introducing a strong model that greatly enhances the precision and flexibility of facial expression recognition systems. Our solution utilizes deep learning frameworks, particularly convolutional neural networks (CNNs), strengthened by domain adaptation techniques to improve performance in various environmental conditions. We also integrate multi-modal data fusion and advanced preprocessing algorithms to reduce the impact of environmental inconsistencies. Through extensive testing, our model demonstrates exceptional accuracy and resilience.},
        keywords = {Facial Expression Recognition, Happiness, Sadness, Anger, Surprise, Disgust, Fear, Deep Learning, CNNs, Domain Adaptation.},
        month = {October},
        }