FACE MASK DETECTION UNDER LOW LIGHT CONDITION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Naufal Muhammad Athif - [ https://orcid.org/0000-0003-0757-7534 ]
Febriyanti Sthevanie
Kurniawan Nur Ramadhan


DOI: https://doi.org/10.29100/jipi.v8i1.3324

Abstract


The COVID-19 pandemic has been around for 3 years, and the virus is still spreading until now and using mask is an alternative for people to not get infected, but some people tend to let go of the mask for inconvenience reasons, especially under low light conditions which is difficult for humans to identify. Thus, this paper proposed and implemented a face mask detection model which can accurately detect a person that using a mask or not in such a condition as low light by using Convolutional Neural Network (CNN) architecture with OpenCV, TensorFlow and Keras. To achieve this, the first step is to transform the data by using Python Imaging Library (PIL) to create a low light image, then we process the data by using Contrast Limited Adaptive Histogram Equalization and with Gamma Correction. The second step is to augment the data by using TensorFlow ImageDataGenerator and define the CNN model. The final step is to create the face mask prediction by using Haar Cascade Algorithm to detect the face mask. The results of this research shows that CNN model can be trained with a recreational low light images to detect face mask under low light conditions. The result of the model produced an accuracy of 98%.

Keywords


Face Mask Detection; Low Light; Keras; TensorFlow; PIL

Full Text:

PDF

Article Metrics :

References


P. Nagrath, R. Jain, A. Madan, R. Arora, P. Kataria and J. Hemanth, "SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2," Sustain Cities Soc., 2020.

A. Das, M. W. Ansari and R. Basak, "Covid-19 Face Mask Detection Using TensorFlow, Keras and OpenCV," IEEE, India, 2020.

S. Sethi, M. Kathuria and T. Kaushik, "Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread," J Biomed Inform., 2021.

J. Yu, X. Hao and P. He, "Single-stage Face Detection under Extremely Low-light Conditions," in IEEE, Montreal, BC, Canada, 2021.

W. Chen and T. Shah, "Exploring Low-light Object Detection Techniques," arXiv, vol. 1, no. Computer Vision and Pattern Recognition (cs.CV), p. 5, 2021.

L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Samantamaria, M. A. Fadhel, M. Al-Amidie and L. Farhan, "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, vol. 8, no. 1, 2021.

T. Gorach, "DEEP CONVOLUTIONAL NEURAL NETWORKS- A REVIEW," International Research Journal of Engineering and Technology (IRJET), vol. 05, no. 07, pp. 439-452, 2018.

N. A. Samat, M. N. B. Mohd Salleh and H. Ali, "The Comparison of Pooling Functions in Convolutional Neural Network for Sentiment Analysis Task," in Recent Advances on Soft Computing and Data Mining, Springer, 2020, pp. 202-210.

H. Gholamalinejad and H. Khosravi, Pooling Methods in Deep Neural Networks, a Review, 2020.

P. Podrzaj and S. Simoncic, "IMAGE PROCESSING CAPABILITIES OF PYTHON," International Journal of Mechanical and Production Engineering, vol. 6, no. 10, pp. 77-81, 2018.

S. Sudhakar, "Histogram Equalization," Towards Data Science, 10 July 2017. [Online]. Available: https://towardsdatascience.com/histogram-equalization-5d1013626e64#:~:text=Histogram%20Equalization%20is%20a%20computer,intensity%20range%20of%20the%20image.. [Accessed 1 August 01].

M. G. W. A.-S. I. S. Irem Doken, Histogram Equalization Of The Image, arXiv, 2021.

K. S. Htoon, "A Tutorial to Histogram Equalization," medium, 19 August 2020. [Online]. Available: https://medium.com/@kyawsawhtoon/a-tutorial-to-histogram-equalization-497600f270e2. [Accessed 2 August 2022].

Pintusaini, "Adaptive Histogram Equalization in Image Processing Using MATLAB," MATLAB image-processing, p. 1, 22 November 2021.

R. Sachdeva, Sonam and H. Sharma, "Face Mask Detection System," International Journal of Scientific and Engineering Research, 2020.

F. Amer and M. S. H. Al-Tamimi, "Face Mask Detection Methods and Techniques: A Review," ResearchGate, pp. 3812-3825, 2022.

J. D. Novakovic, A. Veljovic, S. S. Illic, Z. Papic and M. Tomovic, "Evaluation of Classification Models in Machine Learning," Theory and Applications of Mathematics & Computer Science 7, vol. 1, pp. 39-46, 2017.


slot gacor slot