Browse > Article
http://dx.doi.org/10.5762/KAIS.2021.22.5.251

A Flexible Model-Based Face Region Detection Method  

Jang, Seok-Woo (Department of Software, Anyang University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.5, 2021 , pp. 251-256 More about this Journal
Abstract
Unlike general cameras, a high-speed camera capable of capturing a large number of frames per second can enable the advancement of some image processing technologies that have been limited so far. This paper proposes a method of removing undesirable noise from an high-speed input color image, and then detecting a human face from the noise-free image. In this paper, noise pixels included in the ultrafast input image are first removed by applying a bidirectional filter. Then, using RetinaFace, a region representing the person's personal information is robustly detected from the image where noise was removed. The experimental results show that the described algorithm removes noise from the input image and then robustly detects a human face using the generated model. The model-based face-detection method presented in this paper is expected to be used as basic technology for many practical application fields related to image processing and pattern recognition, such as indoor and outdoor building monitoring, door opening and closing management, and mobile biometric authentication.
Keywords
Face Model; Learning; Ground Truth; Pattern Recognition; Object Region;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, "RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, pp. 5203-5212, June 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00525   DOI
2 Z. Zhou, Z. He, Y. Jia, J. Du, L. Wang, and Z. Chen, "Context Prior-Based with Residual Learning for Face Detection: A Deep Convolutional Encoder-Decoder Network," Signal Processing: Image Communication, Vol.88, pp. 1-13, July 2020. DOI: https://doi.org/10.1016/j.image.2020.115948   DOI
3 J. Geng, W. Jiang, and X. Deng, "Multi-Scale Deep Feature Learning Network with Bilateral Filtering for SAR Image Classification," ISPRS Journal of Photogrammetry and Remote Sensing, Vol.167, pp. 201-213, July 2020. DOI: https://doi.org/10.1016/j.isprsjprs.2020.07.007   DOI
4 Y. Liu and J. Chen, "Unsupervised Face Frontalization for Pose-Invariant Face Recognition," Image and Vision Computing, Vol.106, pp. 1-10, December 2020. DOI: https://doi.org/10.1016/j.imavis.2020.104093   DOI
5 L. Yu and B. Pan, "Full-Frame, High-Speed 3D Shape and Deformation Measurements Using Stereo-Digital Image Correlation and a Single Color High-Speed Camera," Optics and Lasers in Engineering, Vol.95, pp. 17-25, August 2017. DOI: https://doi.org/10.1016/j.optlaseng.2017.03.009   DOI
6 R. Chen, Z. Li, K. Zhong, X. Liu, Y. J. Chao, and Y. Shi, "Low-Speed-Camera-Array-Based High-Speed Three-Dimensional Deformation Measurement Method: Principle, Validation, and Application," Optics and Lasers in Engineering, Vol.107, pp. 21-27, March 2018. DOI: https://doi.org/10.1016/j.optlaseng.2018.03.009   DOI
7 S. Zafeiriou, C. Zhang, and Z. Zhang, "A Survey on Face Detection in the Wild: Past, Present and Future," Computer Vision and Image Understanding, Vol.138, pp. 1-24, 2015. DOI: https://doi.org/10.1016/j.cviu.2015.03.015   DOI
8 J. Zhang, X. Wu, S. C. H. Hoi, and J. Zhua, "Feature Agglomeration Networks for Single Stage Face Detection," Neurocomputing, Vol.380, pp. 180-189, March 2020. DOI: https://doi.org/10.1016/j.neucom.2019.10.087   DOI
9 U. Mahbub, S. Sarkar, and R. Chellappa, "Partial Face Detection in the Mobile Domain," Image and Vision Computing, Vol.82, pp. 1-17, January 2019. DOI: https://doi.org/10.1016/j.imavis.2018.12.003   DOI
10 H. Zhang, X. Wang, J. Zhu, and C.-C. J. Kuo, "Fast Face Detection on Mobile Devices by Leveraging Global and Local Facial Characteristics," Signal Processing: Image Communication, May 2019. DOI: https://doi.org/10.1016/j.image.2019.05.016   DOI