Deep Learning-Based Face Detection and Recognition System
Face detection and recognition systems have recently achieved encouraging results using deep learning especially Convolutional Neural Network (CNN). Face detection and recognition system have many challenges in unconstrained environments that decrease the accuracy, for overcoming these challenges a deep learning-based features combination has been proposed for face recognition. The scheme performs feature-level combination by applying two pre-trained InceptionNet-v1 and VggNet-16 models as deep feature extractors. First, faces are detected and aligned using Multi-Task Convolutional Neural Networks (MTCNN) face detector then the deep features are extracted from a face image using each individually pre-trained CNN. Second, features obtained from InceptionNet-v1 and VggNet-16 models are combined using the serial-feature combinations method. Finally, a classification task is perform using a multiclass Support Vector Machine (SVM) classifier. Experiments on the following datasets: VggFace2, LFW, Essex, and ORL, indicate the efficacy of the proposed system as the combination of the two pretrained CNN models improves performance. The combination strategy, in particular, yields an accuracy of 95.33% to 99.29% on all datasets.
References:  N. Ortiz, R.D. Hernández, R. Jimenez, Survey of biometric pattern recognition via machine learning techniques. Contemp. Eng. Sci. 11(34), 1677–1694, 2018.  K. Sundararajan, D.L.Woodard, Deep learning for biometrics : a survey. ACMComput. Surv. 51(3), 2018.
M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, CloudID: Trustworthy cloud-based and cross enterprise biometric identification. Expert Systems with Applications. 42(21): p. 7905-7916, 2015.  M.O. Oloyede, S. Member, G.P. Hancke, Unimodal and multimodal biometric sensing systems: a review. IEEE Access 4, 7532–7555,2016.  M. Taskiran, N. Kahraman, and C. E. Erdem, “Face recognition: Past, present and future (a review),” Digit. Signal Process. A Rev. J., vol. 106, p. 102809, 2020.  Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors (Switzerland), vol. 20, no. 2, 2020.  M. Chihaoui, A. Elkefi, W. Bellil, and C. Ben Amar, “A survey of 2D face recognition techniques,” Computers, vol. 5, no. 4, pp. 1–28, 2016.  F. Tabassum, M. Imdadul Islam, R. Tasin Khan, and M. R. Amin, “Human face recognition with combination of DWT and machine learning,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020.  S. Bajpai and G. Mishra, “Real Time Face Recognition with limited training data: Feature Transfer Learning integrating CNN and Sparse Approximation,” 2021.  Y. X. Yang, C. Wen, K. Xie, F. Q. Wen, G. Q. Sheng, and X. G. Tang, “Face recognition using the SR-CNN model,” Sensors (Switzerland), vol. 18, no. 12, 2018.  J. Li, T. Qiu, C. Wen, K. Xie, and F. Q. Wen, “Robust face recognition using the deep C2D-CNN model based on decision-level fusion,” Sensors (Switzerland), vol. 18, no. 7, pp. 1–27, 2018.  P. Kamencay, M. Benco, T. Mizdos, and R. Radil, “A new method for face recognition using convolutional neural network,” Adv. Electr. Electron. Eng., vol. 15, no. 4 Special Issue, pp. 663– 672, 2017.  S. Guo, S. Chen, and Y. Li, “Face recognition based on convolutional neural network & support vector machine,” 2016 IEEE Int. Conf. Inf. Autom. IEEE ICIA 2016, no. August, pp. 1787–1792, 2017.  L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021.  L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014.  P. Kamencay, M. Benco, T. Mizdos, and R. Radil, “A new method for face recognition using convolutional neural network,” Adv. Electr. Electron. Eng., vol. 15, no. 4 Special Issue, pp. 663– 672, 2017.  A. Simonyan, Karen and Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv Prepr. arXiv1409.1556, 2014.  C. Szegedy, W. Liu, Y. Jia et al. (2015) Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.  Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "Vggface2: A dataset for recognizing faces across pose and age," in 2018 13th IEEE, pp. 67-74, 2018, [Online]. Available: https://www .robots .ox.ac.uk /~vgg /data /vgg_face2/. [Accessed: 23-Sept-2020].  G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” 2007, [Online]. Available: https://hal.inria.fr/inria-00321923. [Accessed: 18-Sept-2020].  Libor Spacek’s Facial Images Databases “Face 96 Image Database”, [Online]. Available: http://www.cmp.felk.cvut.cz/spacelib/faces/faces96.html. [Accessed: 22-Apr-2020].
“ORL face database.”, [Online]. Available: http://www.uk.research.att.com /facedatabase.html. [Accessed: 06-Apr-2020].
Copyright (c) 2022 Journal of Education for Pure Science- University of Thi-Qar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Journal of education for Pure Science (Jeds), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (Jeds), University of Thi-Qar.
Journal of education for Pure Science (Jeds), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (Jeds), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.