Abstract
In this paper, we consider two multi-face recognition systems using deep and machine learning algorithms. Specifically, one is based on the Haar cascade algorithm coupled with the Local Binary Patterns (LBP) classification approach and the other one on the Histogram of Orianted Gradients (HOG) descriptors coupled with the Convolutional Neural Network (CNN) algorithm. We also carry out an exhaustive comparison between the two systems. The simulation results show clearly that both systems have high face detection rates. However, the deep learning-based system outperforms the machine learning-Based system in the face recognition task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pandian, D., Fernando, X., Baig, Z., Shi, F.: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) (2019)
Wagh, P., Chaudhari, J.: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). S.l.: IEEE (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 815–823, June 2015. https://doi.org/10.1109/CVPR.2015.7298682
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a general-purpose face recognition library with mobile applications. School of Computer Science Carnegie Mellon University Pittsburgh, June 2016
Chien, J.-T., Wu, C.-C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Mach. Intell 24(12), 1644–1649 (2002). https://doi.org/10.1109/TPAMI.2002.1114855
Shi, S.: Emgu CV Essentials: Develop Your Own Computer Vision Application Using the Power of Emgu CV. Packt Publ, Birmingham (2013)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA, vol. 1, pp. I-511–I‑518 (2001). https://doi.org/10.1109/CVPR.2001.990517
Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 179–186. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30548-4_21
Winarno, E., Al Amin, I.H., Februariyanti, H., Adi, P.W., Hadikurniawati, W., Anwar, M.T.: Attendance system based on face recognition system using CNN-PCA method and real-time camera. In: 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 301–304. Yogyakarta, Indonesia (2019). https://doi.org/10.1109/ISRITI48646.2019.9034596
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alane, B., Saad, B. (2022). Multi-face Recognition Systems Based on Deep and Machine Learning Algorithms. In: Ragab Hassen, H., Batatia, H. (eds) Proceedings of the International Conference on Applied CyberSecurity (ACS) 2021. ACS 2021. Lecture Notes in Networks and Systems, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-95918-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-95918-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95917-3
Online ISBN: 978-3-030-95918-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)