Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Chamandeep Vimal, Neeraj Shirivastava

Chamandeep Vimal and Neeraj Shirivastava. Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network. International Journal of Computer Applications 183(50):16-21, February 2022. BibTeX

	author = {Chamandeep Vimal and Neeraj Shirivastava},
	title = {Face and Face-mask Detection System using VGG-16 Architecture based on Convolutional Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2022},
	volume = {183},
	number = {50},
	month = {Feb},
	year = {2022},
	issn = {0975-8887},
	pages = {16-21},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2022921700},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Face recognition can be used in several applications such as in surveillance, identification in login system and personalized technology. The challenge of the face detection system is the non-frontal face position and the use of accessories that cover the face area; even conventional detection systems that rely on facial features are difficult to get high accuracy. The proposed system can overcome these problems and it can detect human face with mask also. The deep learning system can recognize facial features with complex backgrounds. The VGG16 architecture based on convolutional neural network with shallow layers to produce light computing then the system can work real-time. Multiple layer detection on the last feature map is used to detect varied face sizes. The system result shows sequential images of face localization with 93% accuracy.


  1. G. J. Chowdary, N. S. Punn, S. K. Sonbhadra and S. Agarwal, Face mask detection using transfer learning of inceptionv3, 2020.
  2. M. Loey, G. Manogaran, M. H. N. Taha and N. E. M. Khalifa, "A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic", Measurement, vol. 167, pp. 108288, 2021.
  3. Yamashita, R., Nishio, M., Do, R.K.G. et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018).
  4. Z. Zhang, M.J. Lyons, M. Schuster, S. Akamatsu, Comparison be- tween geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron, in: IEEE International Con- ference on Automatic Face & Gesture Recognition (FG), 1998.
  5. Y. Tian, Evaluation of face resolution for expression analysis, in: CVPR Workshop on Face Processing in Video, 2004.
  6. M. Abdulrahman and A. Eleyan, “Facial expression recognition us- ing Support Vector Machines,” 2015 23nd Signal Processing and Com- munications Applications Conference (SIU), Malatya, 2015, pp. 276-279.
  7. Paul Viola , Michael Jones, “Robust Real-time Face Detection”, International Journal of Computer Vision 57(2), 137–154, 2004.
  8. S. Saypadith and S. Aramvith, "Real-Time Multiple Face Recognition using Deep Learning on Embedded GPU System," 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA, 2018, pp. 1318-1324.
  9. Ya Wang, Tianlong Bao, Chunhui Ding and Ming Zhu, "Face recognition in real-world surveillance videos with deep learning method," 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, 2017, pp. 239-243.
  10. K. Zhang, Z. Zhang, Z. Li and Y. Qiao, "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks," in IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, Oct. 2016
  11. Matthew D Zeiler, Rob Fergus, “Visualizing and Understanding Convolutional Networks”, ECCV 2014: Computer Vision – ECCV 2014 pp 818-833.
  12. Redmon, Joseph & Farhadi, Ali. YOLOv3: An Incremental Improvement. Technical report. arXiv. 2018.
  13. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017.


Face detection, Real-time, CPU, Multiple layer, Deep learning