Detection of Human Faces Covered with disguise and Makeup
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.839Keywords:
Disguised/Makeup face, Face detection, Facial parts, Machine learning, landmark points, HOG SVM detectorAbstract
Face detection is kind of the identification. When we look at someone's face, we can get information like his or her gender and age. Face detection research has exploded in popularity during the last few decades. Starting with algorithms that can detect faces in constrained environments, today's face detection systems can attain extremely great accuracies at the large scale unconstrained facial datasets. While new algorithms continue to increase performance, the majority of face detection systems are vulnerable to failure when subjected to disguise and cosmetics alterations, which is one of the most difficult covariates to overcome. In this article, the database of disguised and makeup faces (DMFD) is employed. In order to address this issue, we detected the location and size of the facial in the image by using Histogram of Oriented Gradients (HOG) + Linear SVM Machine Learning detector on the Disguise and makeup face database (DMFD).This approach is effective and can detect any disguise and makeup faces in the complex background and illumination variation. The results shows the effectiveness of the face detection system on a database (DMFD) and it provided better results of (99.3%).
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References
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[23] Wu, Yue, and Qiang Ji. "Facial landmark detection: A literature survey." International Journal of Computer Vision 127, no. 2 (2019): 115-142.
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[26] Wang, Tsung Ying, and Ajay Kumar. "Recognizing human faces under disguise and makeup." In 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1-7. IEEE, 2016.
[2] Dong, Zuolin, Jiahong Wei, Xiaoyu Chen, and Pengfei Zheng. "Face detection in security monitoring based on artificial intelligence video retrieval technology." IEEE Access 8 (2020): 63421-63433.
[3] Hung, Bui Thanh. "Face Recognition Using Hybrid HOG-CNN Approach." In Research in Intelligent and Computing in Engineering, pp. 715-723. Springer, Singapore, 2021.
[4] Mahmoud, Hanan A. Hosni, and Hanan Abdullah Mengash. "A novel technique for automated concealed face detection in surveillance videos." Personal and Ubiquitous Computing 25, no. 1 (2021): 129-140.
[5] Pooja, Saini, and Saini Preeti. "Face Mask Detection Using AI." In Predictive and Preventive Measures for Covid-19 Pandemic, pp. 293-305. Springer, Singapore, 2021.
[6] Minaee, Shervin, Ping Luo, Zhe Lin, and Kevin Bowyer. "Going Deeper Into Face Detection: A Survey." arXiv preprint arXiv:2103.14983 (2021).
[7] Zorin, Arsenii, and Nikolay Abramov. "Disguised Face Detection." In Information Science and Applications, pp. 509-517. Springer, Singapore, 2020.
[8] Weblink for downloading The Hong Kong Polytechnic University Disguise and Makeup Faces Database described in this paper, 2016, available http://www.comp.polyu.edu.hk/~csajaykr/DMFaces.htm
[9] Farfade, Sachin Sudhakar, Mohammad J. Saberian, and Li-Jia Li. "Multi-view face detection using deep convolutional neural networks." In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 643-650. 2015.
[10] Li, Haoxiang, Zhe Lin, Xiaohui Shen, Jonathan Brandt, and Gang Hua. "A convolutional neural network cascade for face detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5325-5334. 2015.
[11] Kumaar, Saumya, Ravi M. Vishwanath, S. N. Omkar, Abrar Majeedi, and Abhinandan Dogra. "Disguised facial recognition using neural networks." In 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), pp. 28-32. IEEE, 2018.
[12] Abhila, A. G., and S. H. Sreeletha. "A deep learning method for identifying disguised faces using AlexNet and multiclass SVM." Int Res J Eng Technol 5, no. 07 (2018).
[13] Tamgale, Sumit Baburao, and Suraj Nandkishor Kothawade. "Application of deep convolutional neural network to prevent ATM fraud by facial disguise identification." In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-5. IEEE, 2017.
[14] Al-Jbaar, Mamoon A., Mohammad JM Zedan, Mohab T. Thameen, and Osamah A. Mahdi. "Face Recognition Security System Based on Convolutional Neural Networks." (2020).
[15] Li, Chong, Rong Wang, Jinze Li, and Linyu Fei. "Face detection based on YOLOv3." In Recent Trends in Intelligent Computing, Communication and Devices, pp. 277-284. Springer, Singapore, 2020.
[16] Zereen, Aniqua Nusrat, Sonia Corraya, Matthew N. Dailey, and Mongkol Ekpanyapong. "Two-Stage Facial Mask Detection Model for Indoor Environments." In Proceedings of International Conference on Trends in Computational and Cognitive Engineering, pp. 591-601. Springer, Singapore, 2021.
[17] Hung, Bui Thanh. "Face Recognition Using Hybrid HOG-CNN Approach." In Research in Intelligent and Computing in Engineering, pp. 715-723. Springer, Singapore, 2021.
[18] Jacob, Melvin Paul. "COMPARISON OF POPULAR FACE DETECTION AND RECOGNITION TECHNIQUES." International Research Journal of Modernization in Engineering Technology and Science, e-ISSN: 2582-5208 , 2021.
[19] AbdulAmeer AbdulJabbar, I., & Ali Yakoob, Z. (2018). Hybrid techniques to improve face recognition based on features extraction methods and Haar discrete wavelet transformation. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(2), Comp Page 36 - 46.
[20] Tahir Al-azawi, F., & Abdulrahman, A. (2020). Face Detection By some Methods based on MATLAB. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(4), Comp Page 12 -17.
[21] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol. 1, pp. 886-893. Ieee, 2005.
[22] Dadi, Harihara Santosh, and GK Mohan Pillutla. "Improved face recognition rate using HOG features and SVM classifier." IOSR Journal of Electronics and Communication Engineering 11, no. 04 (2016): 34-44.
[23] Wu, Yue, and Qiang Ji. "Facial landmark detection: A literature survey." International Journal of Computer Vision 127, no. 2 (2019): 115-142.
[24] Li, Xuan, Yidan Xu, Qi Lv, and Yong Dou. "Affine-transformation parameters regression for face alignment." IEEE Signal Processing Letters 23, no. 1 (2015): 55-59.
[25] “Face Alignment for Face Recognition in Python within OpenCV” [Online]. Available: https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/.
[26] Wang, Tsung Ying, and Ajay Kumar. "Recognizing human faces under disguise and makeup." In 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1-7. IEEE, 2016.
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Published
2021-09-02
How to Cite
Al-ghanim, F. J., & Al-juboori, A. mohsin. (2021). Detection of Human Faces Covered with disguise and Makeup. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 68 – 77. https://doi.org/10.29304/jqcm.2021.13.3.839
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Computer Articles
