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Tytuł artykułu

Detection of human faces in thermal infrared images

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The presented study concerns development of a facial detection algorithm operating robustly in the thermal infrared spectrum. The paper presents a brief review of existing face detection algorithms, describes the experiment methodology and selected algorithms. For the comparative study of facial detection three methods presenting three different approaches were chosen, namely the Viola-Jones, YOLOv2 and Faster-RCNN. All these algorithms were investigated along with various configurations and parameters and evaluated using three publicly available thermal face datasets. The comparison of the original results of various experiments for the selected algorithms is presented.
Rocznik
Strony
307--321
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wzory
Twórcy
  • Military University of Technology, Institute of Optoelectronics, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
  • Military University of Technology, Institute of Optoelectronics, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
  • Military University of Technology, Institute of Optoelectronics, gen. Sylwestra Kaliskiego 2, 00-908 Warszawa, Poland
Bibliografia
  • [1] Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active Contour Models. International Journal of Computer Vision, 1, 321-331. https://doi.org/10.1007/BF00133570
  • [2] Crowley, J. L., & Coutaz, J, (1997). Vision for Man Machine Interaction. Robotics and Autonomous Systems, 19, (3-4), 347-358. https://doi.org/10.1016/S0921-8890(96)00061-9
  • [3] Sakai, T., Nagao, M., & Kanade, T. (1972). Computer Analysis and Classification of Photographs of Human Faces. Proceedings of First USA-JAPAN Computer Conference, Japan, 55-62.
  • [4] Viola, P. & Jones, M.J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision, 57, 137-154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  • [5] Lin, S., Cai, L., Lin, X., & Ji, R. (2016). Masked face detection via a modified LeNet. Neurocomputing, 218, 197-202. https://doi.org/10.1016/j.neucom.2016.08.056
  • [6] Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499-1503. https://doi.org/10.1109/LSP.2016.2603342
  • [7] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C (2016). SSD: Single Shot MultiBox Detector. Proceedings of European Conference on Computer Vision, The Netherlands, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
  • [8] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 779-788. https://doi.org/10.1109/CVPR.2016.91
  • [9] Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: Towards Real-Time Detection with Region Proporsal Networks. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence, USA, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
  • [10] Dai, J., Li, Y., He, K., & Sun, J. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Spain, 397-387. https://arxiv.org/abs/1605.06409
  • [11] Kim, K.H., Hing, S., Roh, B., Cheon, Y., & Park, M. (2016). PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection. Proceeding of Conference on Computer Vision and Pattern Recognition, USA. https://arxiv.org/abs/1608.08021
  • [12] Vu, T. H., Osokin, A., & Laptev, I. (2015). Context-Aware CNNs for Person Head Detection. Proceedings of IEEE International Conference on Computer Vision, Chile, 2893-2901. https://doi.org/10.1109/ICCV.2015.331
  • [13] Qin, H., Yan, J., Li, X., & Hu, X. (2016). Joint Training of Cascaded CNN for Face Detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, USA, 3456-3465. https://doi.org/10.1109/CVPR.2016.376
  • [14] Jiang, H., & Miller, E. L. (2017). Face Detection with the Faster R-CNN. Proceedings of 12th IEEE International Conference on Automatic Face & Gesture Recognition, USA, 650-657. https://doi.org/10.1109/FG.2017.82
  • [15] Mekyska, J., Duro, V. E., & Zanuy, M. F. (2010). Face Segmentation: A comparison between visible and thermal images. Proceedings of 44th Annual 2010 IEEE International Carnahan Conference on Security Technology, USA, 185-189. https://doi.org/10.1109/CCST.2010.5678709
  • [16] Kopaczka, M., Nestler, J., & Merhof, D. (2017). Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine Learning-Based Approaches. Proceedings of International Conference on Advanced Concepts for Intelligent Vision Systems, Belgium, 518-529. https://doi.org/10.1007/978-3-319-70353-4_44
  • [17] Ma, C., Thanh, T. N., Uchiyama, H., Nagahara, H., Shimada, A., & Taniguchi, R. I. (2017). Adapting Local Features for Face Detection in Thermal Image, Sensors, 17(12). https://doi.org/10.3390/s17122741
  • [18] Panasiuk, J., Prusaczyk, P., Grudzień, A., & Kowalski, M., (2020). High-resolution thermal face dataset for face and expression recognition. Metrology and Measurement Systems, 27(3), 399-415. https://doi.org/10.24425/mms.2020.134591
  • [19] Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, USA, 886-893. https://doi.org/10.1109/CVPR.2005.177
  • [20] Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
  • [21] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, USA, 658-666. https://doi.org/10.1109/CVPR.2019.00075
  • [22] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, USA, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
  • [23] Szegedy, Ch., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, USA, 2818-2826. https://doi.org/10.1109/CVPR.2016.308
  • [24] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, USA, 770-778. https://doi.org/10.1109/CVPR.2016.90
  • [25] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25(2), 84-90. https://doi.org/10.1145/3065386
  • [26] Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of 3rd International Conference on Learning Representations, USA. https://arxiv.org/abs/1409.1556
  • [27] Redmon, J. & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. https://arxiv.org/abs/1804.02767
  • [28] Kowalski, M., Grudzień A. (2018). High-resolution thermal face dataset for face and expression recognition. Metrology and Measurement Systems, 25(2), 403-415. https://doi.org/10.24425/119566
  • [29] Sequeira, A. F., Chen, L., Ferryman, J., Galdi, C., Chiesa, V., Dugelay, J. L., Maik, P., Gmitrowicz, P., Szklarski, Ł., Prommegger, B., Kauba, C., Kirchgasser, S., Uhl, A., Grudzień, A., & Kowalski, M. (2018). PROTECT Multimodal DB: a multimodal biometrics dataset envisaging Border Control. Proceedings of International Conference of the Biometrics Special Interest Group, Germany, 1-5. https://doi.org/10.23919/BIOSIG.2018.8552926
  • [30] Espinosa-Duró, V., Faundez-Zanuy, M., & Mekyska, J. (2013). A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums. Cognitive Computation, 5, 119-135. https://doi.org/10.1007/s12559-012-9163-2
  • [31] Espinosa-Duró, V., Faundez-Zanuy, M., Mekyska J., Monte-Moreno, E. (2010). A Criterion for Analysis of Different Sensor Combinations with an Application to Face Biometrics. Cognitive Computation, 2, 135-141. https://doi.org/10.1007/s12559-010-9060-5
Uwagi
1. This research was funded by the Military University of Technology; grant number ZBW/08-894/2020/WAT.
2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-06b571bf-541c-4848-8feb-cd8d1a498b98
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