Face and Emotion Recognition using Deep Learning with Convolutional Neural Networks in Industry Application

• Published in: 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL) pp. 438 - 443
• Main Authors: O, Derrick Samuel, S, Stewart Kirubakaran, Edwin, E. Bijolin, P, Santhiya, Thanka, M. Roshni, Ebenezer, V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13-03-2024
• Subjects: Analytical models; CNN; Data models; Deep learning; Emotion recognition; Image Classification; Industries; Jupyter Notebook; Keras; Mimetext; OpenCV; Sentiment analysis; smtplib; Solids; TensorFlow
• DOI: 10.1109/ICSADL61749.2024.00077

Today's industry areas have more employees since the field is currently the most popular in the world. Everyone wants to be employed in one of the well-known industries and contribute to humanity's advancement by developing as many new things as possible. However, all of these activities and jobs impose both mental and physical stress. Employees who are unable to maintain their health need to take rest and avoid working extra hours. However, there are many ways in which people may work in a stable environment while still progressing and maintaining a healthy work-life balance. The concept of emotion recognition is not new, and it is applied in a variety of industries. This study analyzes how it helps in the industrial environment. This research investigates how emotion detection may be applied in MNCs and other sectors and discusses how the proposed CNN model helps in the classification of emotions. Here, the emotion is captured and detected using OpenCV, and an Email is sent to the identified recipient over SMTP based on the emotion recognized. Thus, the proposed approach has the potential to benefit people.