Author(s)

Shengbing Hong , Wei Zhan, Jinhui She*

Affiliation(s)

College of computer science, Yangtze University, Jingzhou, Hubei, 434023, China.

Corresponding Author

Jinhui She

ABSTRACT

With the continuous development of computer vision technology, face recognition technology is widely used in identity authentication system. The main research content of this paper is the application of face recognition technology in the swipe card attendance system. We all know that most of the traditional swipe card attendance system is to swipe the electromagnetic card to sign in, which inevitably leads to the phenomenon of signing on behalf of others and forgetting to bring the magnetic card. In order to solve the inconvenience of traditional attendance system, in this paper, we focus on the research of a face recognition system of swiping card attendance. We only need to brush the face to punch in. Considering the disadvantages and low accuracy of traditional pattern recognition methods, this paper adopts deep learning face recognition technology. Firstly, through tensorflow deep learning framework, a separable convolutional neural network is built; secondly, face data sets are trained and classified, and relevant parameters are set to train a more suitable face model; finally, MFC visual programming integration process is used in order to realize the computer-side face recognition attendance system. The accuracy of face recognition technology in this paper can reach 98% in 1-to-1 recognition, which can basically meet the needs of attendance system.

KEYWORDS

Face recognition; Deep learning; Face model; Attendance system; Tensorflow.

CITE THIS PAPER

Hong Shengbing, Zhan Wei, She Jinhui. Research on attendance system of face recognition. Eurasia Journal of Science and Technology. 2020, 2(2): 26-31.

REFERENCES

[1] Xing Jin, Wenyun Sun, Zhong Jin. A discriminative deep association learning for facial expression recognition. International Journal of Machine Learning and Cybernetics, 2020, Vol.11 (3), pp.779-793.

[2] Image conditions for machine-based face recognition of juvenile faces[J]. Ching Yiu Jessica Liu,Caroline Wilkinson. Science & Justice. 2020.

[3] Fahima Tabassum,Md. Imdadul Islam,Risala Tasin Khan,M.R. Amin. Human face recognition with combination of DWT and machine learning[J]. Journal of King Saud University - Computer and Information Sciences,2020.

[4] Atoum Y, Liu Y, Jourabloo A, et al. Face anti-spoofing using patch and depth-based cnns[C]//2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017: 319-328.

[5] Xin Liu, Meina Kan, Wanglong Wu, Shiguang Shan, Xilin Chen, "VIPLFaceNet:An Open Source Deep Face Recognition SDK," Frontier of Computer Science, vol. 11, no. 2, pp. 208–218, 2017.

[6] Long Haiqiang, Tan taizhe. Research on face recognition method based on deep convolution network algorithm [J]. Computer simulation, 2017,34 (1): 322-325.

[7] Chen Lichao, Zhang Xiuqin, pan Lihu, et al. Research on face recognition algorithm in coal mine attendance system [J]. Industrial and mining automation, 2015,41 (4): 69-73.

[8] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on pattern analysis and machine intelligence, 1990, 12(7): 629-639.

[9] Weickert J, Romeny B, Viergever M A. Efficient and reliable schemes for nonlinear diffusion filtering[J]. IEEE transactions on image processing, 1998, 7(3): 398-410.

[10] Wu Yao, Qiu weigen. Face recognition based on improved deep convolution neural network [J]. Computer engineering and design, 2017,38 (8): 2246-2250.

[11] He Changting. Research and implementation of face recognition method in class check-in system [D]. 2018.

[12] Liu Sheng. Design and implementation of classroom face brushing and checking in system of Sichuan Jianyuan [D].

[13] Hou Ruifa, Yang Xiong, Chen Weida, et al. Class attendance system based on face recognition technology [J]. Network security technology and application, 2019, 222 (06): 49-50.

[14] Tang Wenjun, Peng Li, Xi Bowen. A class attendance system based on smart phone [J]. Computer age, 2019, 320 (02): 22-26.

[15] W. Cui, W. Zhan, J. Yu, C. Sun and Y. Zhang, "Face Recognition via Convolutional Neural Networks and Siamese Neural Networks," 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS), Chongqing, China, 2019, pp. 746-750, doi: 10.1109/ICICAS48597.2019.00161.

[16] Han Wei, Inomjon Ramatov, Cui Wanxin, et al. An overview of deep learning target detection algorithms based on candidate regions [J]. Journal of Yangtze University (self SCIENCE EDITION), 2019,16 (5): 108-115.

[17] Chenfan Sun, Wei Zhan, Jinhiu She, Yangyang Zhang, "Object Detection from the Video Taken by Drone via Convolutional Neural Networks", Mathematical Problems in Engineering, vol. 2020, Article ID 4013647, 10 pages, 2020. https://doi.org/10.1155/2020/4013647.

[18] Zhang, Z.; Zhan, W.; He, Z.; Zou, Y. Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics. Insects 2020, 11, 565.

[19] Cui, W. Zhan, J. Yu, C. Sun and Y. Zhang, "Face Recognition via Convolutional Neural Networks and Siamese Neural Networks," 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS), Chongqing, China, 2019, pp. 746-750, doi: 10.1109/ICICAS48597.2019.00161.