• I.Kusuma
• Mr.N.Koteswara Rao

The "Eye Contact Detection Using CNN" project focuses on leveraging Convolutional Neural Networks to develop a system capable of accurately detecting eye contact in images or video frames. This project addresses the importance of eye contact in human communication and aims to enhance human-computer interaction by automating the recognition of this social cue. The project dataset comprises images or video frames annotated with labels indicating the presence or absence of eye contact. CNNs, known for their effectiveness in image-related tasks, are employed to extract features and learn patterns associated with eye contact. Key project stages include data collection, data preprocessing, model architecture design (CNN), model training, and evaluation. The final outcome of the project is an eye contact detection system that can be integrated into various applications, such as video conferencing platforms or human-computer interaction interfaces. This technology contributes to the development of more responsive and intuitive systems that can adapt to users' social cues. In conclusion, the "Eye Contact Detection Using CNN" project showcases the application of deep learning in recognizing a fundamental aspect of human communication.

EYE CONTACT DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

"EYE CONTACT DETECTION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.554-558, June-2024, Available :http://www.jetir.org/papers/JETIRGL06092.pdf

"EYE CONTACT DETECTION USING CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp554-558, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06092.pdf