Real time application to identify the facial emotions using Convolutional Neural Network (CNN) and OpenCV Authors Sahabdeen Aysha Asra Department of Information Technology, Faculty of Humanities and Social Sciences, University of Ruhuna, Sri Lanka. K.H. Ramanayaka Faculty of Humanities and Social Sciences, University of Ruhuna, Sri Lanka. DOI: https://doi.org/10.64383/irjss.202501127 Keywords: Convolutional Neural Network (CNN), Deep Learning Model, Face Emotion Recognition, Python Abstract The notion of real-time human emotion identification utilizing Convolutional Neural Network (CNN)-based digital image processing algorithms is proposed in this study. This work offers important literacy calculations that are engaged in face protestation for exact unique confirmation and byline that may efficiently and capably see feelings from the user's vibes. For the purpose of training a model to recognize facial reactions, large datasets are explored and analyzed. A little experiment is done on a variety of women and men of all ages, races, and colors to describe their sentiments, then variations for diverse faces are identified. Existing studies have achieved reasonable accuracy but often lack real-time capability and struggle with variations in lighting and facial orientation. This study bridges that gap by implementing a real-time CNN–OpenCV-based model. To support this study, CNN is utilized along with the Deep Learning model, OpenCV, Tensorflow, Keras, Pandas, and Numpy. This work has been enhanced in three areas: face placement, acknowledgment, and emotion organization. Moreover, computer vision (using a camera) operations were carried out utilizing programs written in Python. An extensive examination is conducted over a lengthy period of time to identify their interior reactions and identify physiological variations for each face in order to show continuous sufficiency. The findings of the tests show how idealized the face investigation framework is. Finally, extremely high accuracy and real-time measurements of programmed face detection and identification were made. The proposed model achieved an overall accuracy of 89% on the FER2013 dataset, demonstrating strong performance in real-time emotion classification. This technique may be used and is very beneficial in many different fields, including colleges, security, schools, and universities and banking. Author Biographies Sahabdeen Aysha Asra, Department of Information Technology, Faculty of Humanities and Social Sciences, University of Ruhuna, Sri Lanka. Ms. Sahabdeen Aysha Asra is a lecturer in the Department of Information Technology, Faculty of Humanities and Social Sciences, University of Ruhuna. Her academic interests include Information Technology education, research, and student-centered learning. K.H. Ramanayaka, Faculty of Humanities and Social Sciences, University of Ruhuna, Sri Lanka. 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