Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

DOI: http://dx.doi.org/10.15439/2022R18

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 157–163 (2022)

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Abstract. The model of smart door lock using face recognition based on hardware is the Jetson TX2 embedded computer proposed in this paper. In order to recognize the faces, face detection is a very important step. This paper studies and evaluates two methods of face detection, namely Histograms of Oriented Gradients (HOG) method which represents the approach using facial features and Multi-task Cascaded Convolutional Neural Networks method (MTCNN) represents using of deep learning and neural networks. To evaluate these two methods, the experimental model is used to verify the hardware platform, which is the Jetson TX2 embedded computer. The face angle parameter is used to rate the detection level and accuracy for each method. In addition, the experimental model also evaluates the speed of face detection from the camera of these methods. Experimental results show that the average time for face detection by HOG and MTCNN method are respectively 0.16s and 0.58s. For face-to-face frames, both methods detect very well with an accuracy rate of 100\%. However, with various face angles of 30o, 60o, 90o, the MTCNN method gives more accurate results, which is also consistent with published studies. The smart door lock model uses the MTCNN face detection method combined with the Facenet algorithm along with a data set of 200 images for 1 face with accuracy of 99\%.

References

1. N. Dalal, B. Triggs, “Histograms of Oriented Gradients for Human Detection”. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005
2. S K Eng* , H Ali, A Y Cheah, Y F Chong, Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine, IOP Conf. Series: Materials Science and Engineering 705 (2019) 012031, http://dx.doi.org/10.1088/1757-899X/705/1/012031
3. S Yallamandaia, N Purnachand., “A novel face recognition technique using Convolutional Neural Network, HOG, and histogram of LBP features”, IEEE, 2022.
4. D.Lakshmi, R.Ponnusamy, “Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders”, Microprocessors and Microsystems, vol. 82, 2021.
5. S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang and S. Z. Li, "Sˆ 3FD: Single Shot Scale-Invariant Face Detector," in Computer Vision (ICCV), 2017 IEEE International Conference on, 2017
6. Kaipeng Zhang1 Zhanpeng Zhang2 Zhifeng Li1 Yu Qiao1, Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. IEEE Signal Processing Letters (SPL), vol. 23, no. 10, pp. 1499-1503, 2016
7. S. Ren, K. He, R. Girshick and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," in Advances in neural information processing systems, 2015
8. Guangyong Zheng, Yuming Xu, “Efficient face detection and tracking in video sequences based on deep learning”, Information Sciences, vol. 568, pp. 265-285, 2021.
9. P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, pp. 137-154, 2004
10. Chunming Wu, Ying Zhang, “MTCNN and FACENET Based Access Control System for Face Detection and Recognition”, Automatic Control and Computer Sciences, vol. 55, pp. 102-112, 2021