Localization of Eye Region in Infrared Thermal Images using Deep Neural Network

被引:0
作者
Meenakshi, Madura R. [1 ]
Padmapriya, N. [1 ]
Venkateswaran, N. [1 ]
Ravikumar, R. [2 ]
Chelliah, Ramya [2 ]
机构
[1] Sri Sivasubramaniya Nadar Coll Engn, Dept Math, Kalavakkam, Tamil Nadu, India
[2] Tagore Med Coll Hosp, Dept Ophthalmol, Chennai, Tamil Nadu, India
来源
2021 SIXTH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET) | 2021年
关键词
Infrared thermal image; Region of interest; Eye localization; YOLO v2; Deep neural network;
D O I
10.1109/WISPNET51692.2021.9419446
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Infrared (IR) thermal images are produced by recording the thermal radiation emitted from the object. It has been increasingly used in industrial installations, surveillance, evaluating human health, etc. Medical IR thermal images are used as a tool for early diagnosis of certain diseases in the human beings. It can detect the heat patterns of the body as well as irregular blood flow. Compared to visual images, thermal images have low resolution and low contrast. Hence in IR thermal images eye detection is more challenging, for ocular surface disease diagnosis. This article proposes an automatic eye localization method from IR thermal images using YOLO v2 object detector. The performance of eye localization in IR thermal images using YOLO v2 shows mean average precision 97% and mean intersection over union (IoU) 90% for test images.
引用
收藏
页码:446 / 450
页数:5
相关论文
共 16 条
  • [1] Beitzel S M., 2009, Encyclopedia of database systems, P1691, DOI [DOI 10.1007/978-0-387-39940-9492, 10.1007/978-0-387-39940-9 _492, DOI 10.1007/978-0-387-39940-9_492, 10.1007/978-0-387-39940-9_492]
  • [2] Chen F., P INT C ADV COMP TEC, P111, DOI [10.5220/0008096201110116, DOI 10.5220/0008096201110116]
  • [3] FREEMAN RD, 1973, INVEST OPHTH VISUAL, V12, P596
  • [4] Fast R-CNN
    Girshick, Ross
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, : 1440 - 1448
  • [5] Rich feature hierarchies for accurate object detection and semantic segmentation
    Girshick, Ross
    Donahue, Jeff
    Darrell, Trevor
    Malik, Jitendra
    [J]. 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, : 580 - 587
  • [6] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [7] Speed/accuracy trade-offs for modern convolutional object detectors
    Huang, Jonathan
    Rathod, Vivek
    Sun, Chen
    Zhu, Menglong
    Korattikara, Anoop
    Fathi, Alireza
    Fischer, Ian
    Wojna, Zbigniew
    Song, Yang
    Guadarrama, Sergio
    Murphy, Kevin
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 3296 - +
  • [8] Fast eyes detection in thermal images
    Knapik, Mateusz
    Cyganek, Boguslaw
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (03) : 3601 - 3621
  • [9] Marzec M., 2016, Multimedia Tools and Applications, P1, DOI 10.1007/s11042-016-4094-4097
  • [10] YOLO9000: Better, Faster, Stronger
    Redmon, Joseph
    Farhadi, Ali
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 6517 - 6525