Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models

Marcin Kopaczka, Kemal Acar, Dorit Merhof

2016

Abstract

Long wave infrared (LWIR) imaging is an imaging modality currently gaining increasing attention. Facial images acquired with LWIR sensors can be used for illumination invariant person recognition and the contactless extraction of vital signs such as respiratory rate. In order to work properly, these applications require a precise detection of faces and regions of interest such as eyes or nose. Most current facial landmark detectors in the LWIR spectrum localize single salient facial regions by thresholding. These approaches are not robust against out-of-plane rotation and occlusion. To address this problem, we therefore introduce a LWIR face tracking method based on an active appearance model (AAM). The model is trained with a manually annotated database of thermal face images. Additionally, we evaluate the effect of different methods for AAM generation and image preprocessing on the fitting performance. The method is evaluated on a set of still images and a video sequence. Results show that AAMs are a robust method for the detection and tracking of facial landmarks in the LWIR spectrum.

References

  1. Alabort-I-medina, J., Antonakos, E., Booth, J., Snape, P., and Zafeiriou, S. (2014). Menpo: A comprehensive platform for parametric image alignment and visual deformable models. In ACM International Conference on Multimedia, MM 7814, pages 679-682, Orlando, Florida, USA. ACM.
  2. Alkali, A. H., Saatchi, R., Elphick, H., and Burke, D. (2014). Eyes' corners detection in infrared images for real-time noncontact respiration rate monitoring. In WCCAIS 2014, pages 1-5. IEEE.
  3. Antonakos, E., i medina, J. A., Tzimiropoulos, G., and Zafeiriou, S. (2015). Feature-based lucas-kanade and active appearance models. IEEE Transactions on Image Processing, 24(9):2617-2632.
  4. Cootes, T. F., Edwards, G. J., and Taylor, C. J. (2001). Active appearance models. IEEE PAMI, 23(6):681-685.
  5. Dowdall, J., Pavlidis, I. T., and Tsiamyrtzis, P. (2007). Coalitional tracking. Comput. Vis. Image Underst., 106(2-3):205-219.
  6. Filipe, S. and Alexandre, L. A. (2013). Thermal infrared face segmentation: A new pose invariant method. In Pattern Recognition and Image Analysis, pages 632- 639. Springer.
  7. Gault, T. R. and Farag, A. A. (2013). A fully automatic method to extract the heart rate from thermal video. In CVPRW 2013, pages 336-341. IEEE.
  8. Ghiass, R. S., Arandjelovic, O., Bendada, A., and Maldague, X. (2014). Infrared face recognition: A comprehensive review of methodologies and databases. Pattern Recognition, 47(9):2807-2824.
  9. Ghiass, R. S., Arandjelovic, O., Bendada, H., and Maldague, X. (2013). Vesselness features and the inverse compositional aam for robust face recognition using thermal ir. arXiv preprint arXiv:1306.1609.
  10. Gross, R., Matthews, I., and Baker, S. (2005). Generic vs. person specific active appearance models. Image and Vision Computing, 23(12):1080-1093.
  11. Kalal, Z., Mikolajczyk, K., and Matas, J. (2012). Trackinglearning-detection. PAMI, 34(7):1409-1422.
  12. Lahiri, B., Bagavathiappan, S., Jayakumar, T., and Philip, J. (2012). Medical applications of infrared thermography: a review. Infrared Physics & Technology, 55(4):221-235.
  13. Lewis, G. F., Gatto, R. G., and Porges, S. W. (2011). A novel method for extracting respiration rate and relative tidal volume from infrared thermography. Psychophysiology, 48(7):877-887.
  14. Matthews, I. and Baker, S. (2004). Active appearance models revisited. IJCV, 60(2):135-164.
  15. Papandreou, G. and Maragos, P. (2008). Adaptive and constrained algorithms for inverse compositional active appearance model fitting. InCVPR 2008, pages 1-8. IEEE.
  16. Zhou, Y., Tsiamyrtzis, P., Lindner, P., Timofeyev, I., and Pavlidis, I. (2013). Spatiotemporal smoothing as a basis for facial tissue tracking in thermal imaging. IEEE Trans. Biomed. Engineering, 60(5):1280-1289.
  17. Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In CVPR 2012, pages 2879-2886. IEEE.
  18. Zhu, Z., Tsiamyrtzis, P., and Pavlidis, I. (2008). The segmentation of the supraorbital vessels in thermal imagery. In AVSS 2008, pages 237-244.
Download


Paper Citation


in Harvard Style

Kopaczka M., Acar K. and Merhof D. (2016). Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 150-158. DOI: 10.5220/0005716801500158


in Bibtex Style

@conference{visapp16,
author={Marcin Kopaczka and Kemal Acar and Dorit Merhof},
title={Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={150-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005716801500158},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models
SN - 978-989-758-175-5
AU - Kopaczka M.
AU - Acar K.
AU - Merhof D.
PY - 2016
SP - 150
EP - 158
DO - 10.5220/0005716801500158