A Model for Face Recognition using EigenFace Algorithm
Keywords:Feature Descriptor, Covariance Matrix, Eigenface, eigenvector, Linear Algebra
The use of a computer to recognize a person by the means of their face is what is known as face recognition in artificial intelligence. The term biometrics is an umbrella term that includes face recognition as well as signature, fingerprint, eye scanning, gait, and palm print recognition. The principal component analysis technique was used in this paper to extract distinctive features from the faces which are matched with other faces stored in the database and predictive results indicated which faces were recognized and the ones that were not recognized. The accuracy of these techniques was calculated and the principal component analysis technique was found to be 86.3636% accurate and it was concluded that the technique performs better when it comes to face recognition.
Bahurupi, S. P. (2018). Principal component analysis for face recognition. International Journal of Engineering and Advanced Technology, 91-94.
Frey, B. J., Colmenarez, A., & Huang, T. S. (2018, June). Mixtures of local linear subspaces for face recognition. In Proceedings. 20188 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231) (pp. 32-37). IEEE.
Joseph, J. &. (2016). Automatic attendance management system using face recognition. International Journal of Science and Research (IJSR), 327-330.
Meethongjan, K. &. (2017). A Summary of literature review: Face Recognition. In Postgraduate annual research seminar.
Sahoolizadeh, H. &. (2018, September). Face recognition using eigen-faces, fisher-faces and neural networks. In 2018 7th IEEE International Conference on Cybernetic Intelligent Systems (pp. 1-6). IEEE.
Sandhu, P. S. (2019). Face Recognition Using Eigen face Coefficients and Principal Component Analysis. International Journal of Electrical and Electronics Engineering, 498-502.
Sharif, M. M. (2018). A survey: face recognition techniques. Research Journal of Applied Sciences, Engineering and Technology, 4979-4990.
Turk, M. A. (2019, January). Face recognition using eigenfaces. In Proceedings. 2019 IEEE computer society conference on computer vision and pattern recognition (pp. 586-587). IEEE Computer Society.
Wen, Y., He, L., & Shi, P. (2012). Face recognition using difference vector plus KPCA. Digital Signal Processing, 22(1), 140-146.
Zhang, C., Zhou, Z., Sun, H., & Dong, F. (2012, May). Comparison of three face recognition algorithms. In 2012 International Conference on Systems and Informatics (ICSAI2012) (pp. 1896-1900). IEEE.
How to Cite
Copyright (c) 2023 International Scientific Research and Researchers Association (ISRRA)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.