Abstract
In contrast to spoken words, facial expressions communicate a lot of information visually. Since there are so many different ways to portray emotions, it has become difficult to recognize them using computer vision for human–machine interaction. Nowadays, deep learning techniques own a big success in various fields including computer vision. From the background analysis, we found that the recognition of emotions is still difficult and it relies on some advancements in image preprocessing and computer vision techniques. In the proposed system, some image enhancement techniques are being used along with CNN. Our primary goal is to find an emotion that has been exhibited and identify it based on its geometry and physical characteristics. The proposed system uses convolutional neural network (CNN) variants and OpenCV to recognize seven primary human emotions—“anger, disgust, fear, happiness, sadness, surprise, and neutrality”. The suggested system can be applied to programs that analyze human behavior.
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Madhavi, K.R., Vooradi, S., Mounika, P., Yedlla, S., Tangudu, N. (2023). Emotion Analysis Using Convolutional Neural Network. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_44
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DOI: https://doi.org/10.1007/978-981-19-9228-5_44
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