Copyright © 2026 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{171440,
        author = {Sarita Maurya},
        title = {Enhance Face Recognition Algorithm Based on  LBPH &CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3250-3255},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171440},
        abstract = {Automated facial expression recognition is challenge in computer vision domain. Many techniques have been applied to gain accurate and efficient results in identifying face expressions. For monitoring security, treating patients in medical field, Human-machine interaction, marketing research and E- learning are some of the application of facial expression recognition. Feature extraction is the first step in facial expression recognition, followed by classifier to classify input face expressions. Local Binary Pattern is a texture description method that describes the local texture feature of an image in a gray-scale range. Convolutional Neural Networks is one of the most representative network structures in deep learning technology, and it has achieved great success in the field of image processing and recognition. In this paper, facial expression recognition using Efficient Local Binary Pattern (LBP) images and convolutional neural network (CNN) for classification is presented. The proposed algorithm is tested using Cohn-Kanade dataset which results 90% accuracy.},
        keywords = {Facial Expression Recognition, deep learning, Convolutional Neural Network, Local Binary Pattern, Feature Map.},
        month = {December},
        }