Face Detection by Measuring Thermal Value to Avoid Covid-19

Abstract

In this study, custom Single Shot Detection (SSD) was used to detect infected people faces because of Covid-19. It has been suggested to determine around the eyes area where the body temperature of the person most accurate by determining the facial landmarks with Ensemble of Regression Trees (ERT) model on these detected faces. Finally, the thermal value was measured from around the eyes area in a non-contact way using sensor fusion. As a result of the analyzes made, it was observed that the proposed system gave results close to the different measurement methods

Keywords

• SSD
• Facial Landmark
• ERT
• Non-contact
• Sensor Fusion
• Covid-19

Covid-19'u Önlemek İçin Termal Değeri Ölçerek Yüz Tespiti

Abstract

Bu çalışmada, Covid-19 nedeniyle enfekte olmuş kişilerin yüzlerini tespit etmek için özel Single Shot Detection (SSD) modeli kullanıldı. Tespit edilen bu yüzler üzerinde Ensemble of Regresyon Trees (ERT) modeliyle yüz işaret noktaları belirlenerek kişinin vücut sıcaklığının en doğru olduğu göz çevresinin tespit edilmesi önerilmiştir. Son olarak, termal değer, sensör füzyonu kullanılarak temassız bir şekilde göz çevresinden ölçülmüştür. Yapılan analizler sonucunda önerilen sistemin farklı ölçüm yöntemlerine yakın sonuçlar verdiği gözlemlenmiştir.

Keywords

• SSD
• Yüz İşaret Noktaları
• ERT
• temassız
• Sensör Füzyonu
• Covid-19

References

1. Cai, Q., Huang, D., Ou, P., Yu, H., Zhu, Z., Xia, Z., ... & Chen, J. (2020). COVID‐19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy, 75(7), 1742-1752.
2. Ge, S., Li, J., Ye, Q., & Luo, Z. (2017). Detecting masked faces in the wild with lle-cnns. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2682-2690).
3. Hays, J. N. (2005). Epidemics and pandemics: their impacts on human history. Abc-clio.
4. Jiang, H., & Learned-Miller, E. (2017, May). Face detection with the faster R-CNN. In 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017) (pp. 650-657). IEEE.
5. Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1867-1874).
6. Li, D., Menassa, C. C., & Kamat, V. R. (2018). Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography. Energy and Buildings, 176, 246-261.
7. Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5325-5334).
8. Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision (pp. 3730-3738).

9. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector,‖ in European conference on computer vision (ECCV).
10. Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tur, D., ... & Zweig, G. (2014). Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 530-539.
11. Öztürk, Ş., ve Akdemir, B. (2019). Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks. International Journal of Imaging Systems and Technology, 29(3), 234-246.
12. Ranjan, R., Patel, V. M., & Chellappa, R. (2017). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE transactions on pattern analysis and machine intelligence, 41(1), 121-135.
13. Sun, G., Nakayama, Y., Dagdanpurev, S., Abe, S., Nishimura, H., Kirimoto, T., & Matsui, T. (2017). Remote sensing of multiple vital signs using a CMOS camera-equipped infrared thermography system and its clinical application in rapidly screening patients with suspected infectious diseases. International Journal of Infectious Diseases, 55, 113-117.
14. Yang, S., Luo, P., Loy, C. C., & Tang, X. (2015). From facial parts responses to face detection: A deep learning approach. In Proceedings of the IEEE international conference on computer vision (pp. 3676-3684).
15. Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014, September). Facial landmark detection by deep multi-task learning. In European conference on computer vision (pp. 94-108). Springer, Cham.