People authentication through a SVM classifier
Keywords:
Feature extraction, SVM, People authentication
Abstract
In recent years, people’s authentication has taken a significant boom due to technological advances and research developed around the subject. In this process, computer vision techniques are used to process an image or video to determine a person’s identity. In this article, we analyzed related works to the people authentication process, making a deep analysis in the works based on Support Vector Machines (SVM). In the same way, we roughly explained the stages that make up the process of people authentication. Finally, we present a set of experiments performed, using a feature combination based on color, texture, and symmetry. In contrast, SVM is used for the classification stage. This combination of features, together with the classifier, shows to be an alternative to people authentication.
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Almabdy, S., & Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Applied Sciences, 9(20). https://doi.org/10.3390/app9204397
Balamurugan, A., & Suganya, B. (2021). An Efficient Real Time Face Expression Identification System Using SVM. Journal of Physics: Conference Series, 1916(1). https://doi.org/10.1088/1742-6596/1916/1/012229
Bindu, H., & Manjunathachary, K. (2019). Kernel-based scale-invariant feature transform and spherical SVM classifier for face recognition. Journal of Engineering Research, 7(3), 142–160. https://kuwaitjournals.org/jer/index.php/JER/article/view/4177
Chen, H., & Haoyu, C. (2019). Face Recognition Algorithm Based on VGG Network Model and SVM. Journal of Physics: Conference Series, 1229. https://doi.org/10.1088/1742-6596/1229/1/012015
De-la-Torre, M., Granger, E., Radtke, P. V. W., Sabourin, R., & Gorodnichy, D. O. (2015). Partially-supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion, 24. https://doi.org/10.1016/j.inffus.2014.05.006
Dino, H. I., & Abdulrazzaq, M. B. (2019, April). Facial Expression Classification Based on SVM, KNN and MLP Classifiers. 2019 International Conference on Advanced Science and Engineering (ICOASE). https://doi.org/10.1109/ICOASE.2019.8723728
George, M., Sivan, A., Jose, B. R., & Mathew, J. (2019). Real-time single-view face detection and face recognition based on aggregate channel feature. International Journal of Biometrics, 11(3). https://doi.org/10.1504/IJBM.2019.100829
Ghazal, M. T., & Abdullah, K. (2020). Face recognition based on curvelets, invariant moments features and SVM. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 733–739. https://doi.org/10.12928/telkomnika.v18i2.14106
Hu, L., & Cui, J. (2019). Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm. Measurement, 145. https://doi.org/10.1016/j.measurement.2019.02.006
Huang, H., & Zhu, J. (2021). A Short Review of the Application of Machine Learning Methods in Smart Airports. Journal of Physics: Conference Series, 1769. https://doi.org/10.1088/1742-6596/1769/1/012010
Jain, A. K., Ross, A. A., & Nandakumar, K. (2011). Introduction to Biometrics. Springer US. https://doi.org/10.1007/978-0-387-77326-1
Kar, N. B., Babu, K. S., Sangaiah, A. K., & Bakshi, S. (2019). Face expression recognition system based on ripplet transform type II and least square SVM. Multimedia Tools and Applications, 78(4). https://doi.org/10.1007/s11042-017-5485-0
Liu, X., Cheng, X., & Lee, K. (2020). GA-SVM based Facial Emotion Recognition using Facial Geometric Features. IEEE Sensors Journal, 21(10), 11532–11542. https://doi.org/10.1109/JSEN.2020.3028075
Reddy, C. V. R., Reddy, U. S., & Kishore, K. V. K. (2019). Facial Emotion Recognition Using NLPCA and SVM. Traitement Du Signal, 36(1). https://doi.org/10.18280/ts.360102
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). A Semi-automatic Methodology for Facial Landmark Annotation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
Shah, J. H., Sharif, M., Yasmin, M., & Fernandes, S. L. (2020). Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognition Letters, 139. https://doi.org/10.1016/j.patrec.2017.06.021
Singh, S., Singh, D., & Yadav, V. (2020). Face Recognition Using HOG Feature Extraction and SVM Classifier. International Journal of Emerging Trends in Engineering Research, 8(9). https://doi.org/10.30534/ijeter/2020/244892020
Vengatesan, K., Kumar, A., Karuppuchamy, V., Shaktivel, R., & Singhal, A. (2019, December). Face Recognition of Identical Twins Based On Support Vector Machine Classifier. 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https://doi.org/10.1109/I-SMAC47947.2019.9032548
Zhang, B. (2019). Distributed SVM face recognition based on Hadoop. Cluster Computing, 22(S1). https://doi.org/10.1007/s10586-017-1330-5
How to Cite
Aparicio-Arroyo, A. A., Olmos-Pineda, I., & Olvera-López , J. A. (2021). People authentication through a SVM classifier. Revista Colombiana De Computación, 22(2), 48–57. https://doi.org/10.29375/25392115.4299
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Published
2021-12-01
Section
Article of scientific and technological research
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