A Review and Construction of a Real-time Facial Recognition System
The evolution of surveillance technologies allows a reduction in human interaction with the process, since most of the monitoring functions performed by an individual can be replaced by detection and recognition techniques in real-time. This paper proposes the development of a surveillance system, which uses these techniques to identify individuals present within the field of view of camera. A combination of the Histogram of Oriented Gradient and Support Vector Machine techniques is applied for face detection, while a Residual Network is used during the stage of recognizing individuals. This shows the possibility of implementing this set of techniques, even in hardware with processing limitations.
Brahmbhatt, N. R., Prajapati, H. B. and Dabhi, V. K. (2017) ‘Survey and analysis of extraction of human face features’, Innovations in Power and Advanced Computing Technologies, i-PACT, pp. 1–8.
Chuo, Y. H., Sheu, R. K. and Chen, L. C. (2019) ‘Design and Implementation of a Cross-Camera Suspect Tracking System’, International Automatic Control Conference (CACS), pp. 1–6.
Dadi, H. S. and M Pillutla, G. K. (2016) ‘Improved Face Recognition Rate Using HOG Features and SVM Classifier’, IOSR Journal of Electronics and Communication Engineering, Vol. 11(04), pp. 34–44.
Gupta (2016) ‘Face Detection and Recognition using Local’, IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 7923– 7929.
He, K. et al. (2016) ‘Deep residual learning for image recognition’, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778.
Jain, C. et al. (2018) ‘Emotion Detection and Characterization using Facial Features’, 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE, pp. 1–6.
Kazemi, V. and Sullivan, J. (2014) ‘One millisecond face alignment with an ensemble of regression trees’, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1867–1874.
King, D. E. (2009) ‘Dlib-ml: A machine learning toolkit’, Journal of Machine Learning Research, Vol. 10, pp. 1755–1758. King, D. E. (2015) ‘Max-Margin Object Detection (MMOD). Available: http://arxiv.org/abs/1502.00046. Accessed on: Mar., 25, 2020.’, arXiv, pp. 1–8.
Li, H. et al. (2015) ‘A convolutional neural network cascade for face detection’, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5325–5334.
Li, T. et al. (2018) ‘A Research of Character Recognition Based on Residual Neural Network’, IEEE International Conference of Safety Produce Informatization, pp. 804–807.
Liao, S. et al. (2014) ‘A benchmark study of large-scale unconstrained face recognition’, IJCB International Joint Conference on Biometrics, pp. 1–8.
Lin, Z. H. and Li, Y. Z. (2019) ‘Design and Implementation of Classroom Attendance System Based on Video Face Recognition’, IEEE International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS, pp. 385–388.
Patil, A. and Shukla, M. (2014) ‘Implementation of Classroom Attendance System Based on Face Recognition in Class’, International Journal of Advances in Engineering & Technology, Vol. 7(3), pp. 974–979.
Sajjad, M. et al. (2017) ‘Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities’, Future Generation Computer Systems, pp. 1–32.
Valeriani, D. and Poli, R. (2019) ‘Cyborg groups enhance face recognition in crowded environments’, PLOS ONE, Vol. 14(3), pp. 1–17.
Viola, P. and Jones, M. J. (2004) ‘Robust Real-Time Face Detection’, International Journal of Computer Vision, Vol. 57(2), pp. 137–154.
Wazwaz, A. A. et al. (2018) ‘Raspberry Pi and computers-based face detection and recognition system’, IEEE 4th International Conference on Computer and Technology Applications, ICCTA, pp. 171–174.
Wu, Y. and Ji, Q. (2018) ‘Facial Landmark Detection: A Literature Survey’, International Journal of Computer Vision, Vol. 127(2), pp. 115–142.
Xiang, Z., Tan, H. and Ye, W. (2018) ‘The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP’, IEEE Access, Vol. 6, pp. 29306–29318.