• Prof. Balaji A. Chaugule
• Bhakti Sambal
• Janakee Shelke
• Aachal Chimankar
• Unnati Shinde

The "A Deep Learning Model for Facial Expression to Emoji Generation and Statistical Representation" project aims to recognize and categories facial expressions in real time using a convolutional neural network (CNN) architecture. Using computer vision approaches and powerful deep learning algorithms, the system seeks to recognize and analyze emotions expressed in face images taken with a webcam. The detected face parts are then clipped and standardized in size. These processed face photos are then fed into a pre-trained CNN model that has been thoroughly trained on a varied dataset of facial emotions. Leveraging renowned libraries such as TensorFlow, OpenCV for deep learning, image processing and user interface components, respectively, this project amalgamates computer vision techniques and advanced deep learning algorithms to deliver an interactive and captivating platform for real-time emotion recognition. Its versatility extends across various domains encompassing user sentiment analysis, market research, and human- computer interaction.

"A Deep Learning Model for Facial Expression to Emoji Generation and Statistical Representation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.q220-q224, April-2024, Available :http://www.jetir.org/papers/JETIR2404H28.pdf

"A Deep Learning Model for Facial Expression to Emoji Generation and Statistical Representation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppq220-q224, April-2024, Available at : http://www.jetir.org/papers/JETIR2404H28.pdf