Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174816,
        author = {Dr Hariom Upadhyay and Mohd Azad and Shivam Shreevastav},
        title = {EMOTION DETECTOR},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {762-766},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174816},
        abstract = {Emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To maintain the texture picture's edge structure information, the retrieved edge information is placed on each feature image. In this research, several datasets are investigated and explored for training expression recognition models. The purpose of this paper is to make a study on emotion detection and recognition via Machine learning algorithms and deep learning. This research work will present deeper insights into emotion detection and Recognition. It will also highlight the variables that have an impact on its efficacy.},
        keywords = {Convolutional neural network, machine learning, deep learning, computer vision, emotion recognition.},
        month = {March},
        }