Abstract
In recent years, the rapid development of artificial intelligence, Internet of Things, and cloud technology has led to the widespread application of face recognition technology, especially in the face recognition system based on deep learning. At present, the face recognition systems only use a single face recognition model for the identity prediction. Furthermore, systems wouldn’t perform data training automatically when facial images are captured under monitoring. In the other words, these systems are supposed to manually review the prediction result of face images in order to add training to improve the accuracy rate, because there is no other review method for data training to evaluate the prediction result in these systems. Therefore, this paper developed a method using mutual correction of classification models based on two human face neural network models, called the Dual Face Recognition Model Interactive Correction Training System. Inside the system, we use both FaceNet and OpenFace as the core, to build a module update algorithm. The method is to achieve each set of forecast identity label and confidence for a new face image by both Face Net training models individually. And then the method proceeds to compare with these two sets and mutual correct the results. The new face image with mutual correction identity is used for data training. Therefore, system can execute automatic and dynamic updates, and effectively improve classification accuracy. Moreover, thus system has the advantages of real-time training and limiting model size due to the use of its training process on the classification model. Experimental results show that our system has high performance with a small number of training samples. The system can also automatically improve the accuracy rate and AUC value, and lower error sample rate through the system’s modular update algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–4 (2015)
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a general-purpose face recognition library with mobile applications (2016)
Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4(3), 519–524 (1987)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pergamon Pattern Recogn. 29(1), 51–59 (1996)
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
Oo, S.L.M., Oo, A.N.: ASEAN child face recognition system with FaceNet. In: Myanmar Universities’ Research Conference (MURC), pp. 62–68 (2019)
Li, L., Jun, Z., Fei, J., Li, S.: An incremental face recognition system based on deep learning. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 212–215 (2017)
Wang, Y.-K., Zheng, Y.-X., Lin, C.-S.: Classiface: real-time face recognition based on multi-task convolution neural network. Int. J. Sci. Eng. 8(1), 15–28 (2018)
Sun, Y., Wang, X., Tang, X.: Hybrid deep learning for face verification. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1997–2009 (2016)
Santoso, K., Kusuma, G.P.: Face recognition using modified OpenFace. Proc. Comput. Sci. 135, 510–517 (2018)
Acknowledgment
This work was supported by Ministry of Science and Technology (MOST) of Taiwan under Grants MOST 109-2221-E-006-199 and 109-2218-E-006-007. This work was financially supported by the “Intelligent Manufacturing Research Center” (iMRC) in NCKU from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lee, PX., Wang, DC., Tsai, ZJ., Chen, CC. (2021). Develop a Hybrid Human Face Recognition System Based on a Dual Deep Neural Network by Interactive Correction Training. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-73280-6_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73279-0
Online ISBN: 978-3-030-73280-6
eBook Packages: Computer ScienceComputer Science (R0)