Nasal Cavity Detection in Facial Thermal Image for Non-contact Measurement of Breathing

Dai Hanawa, Toshiki Morimoto, Shota Shimazaki, Kimio Oguchi


In this research, we propose a method of improving on the accuracy of detecting nasal cavity location in far infrared images for non-contact measurement of human breathing. We found that although our previous method for far infrared imaging can detect regions that include nasal cavities well, several false alarms occur. In order to reduce false alarms, we propose to apply false alarm classification into our current method. Object detection method based on a boosted cascade of Haar-like feature classifiers are applied to find the candidates of the region including nasal cavities. In false alarm classification, binarize process is employed to segment facial area and background strictly. Based on the result of binarize process, false alarm on background can be classified from the results of detection. 5,100 FIR images are collected to train our nasal cavity detector; we evaluate the number of false alarms and detection failures. The results show that proposed method can reduce false alarm events.

Full Text:



D. Hanawa, T. Morimoto, S. Terada, T. Sakai, S. Shimazaki, K. Igarashi and K. Oguchi, “Nose detection on far infrared image for non-contact measurement of breathing,” in Proc. IEEE-EMBS BHI 2012 [USB-Memory], Hongkong and Shenzhen, China, Jan. 2012, pp.878-881.

Y. Nishida, T. Hori, “Non-invasive and unrestrained monitoring of human respiratory system by sensorized environment,” Proc. IEEE Sensor 2002, Orland, FL, June 2002, pp. 62.4(1)-(6).

Y. Nishida, T. Mori, H. Mizoguchi and T. Sato, “Sleep apnea syndrome diagnosis based on image processing,” J. RSJ, vol.16, no.2, Mar. 1998, pp. 274-281 (in Japanese).

Y. Nishida, M. Takeda, T. Mori, H. Mizoguchi and T. Sato, “Unrestrained and non-invasive monitoring of human’s respiration and posture in sleep using pressure sensors,” J. RSJ, vol.16, no.5, July 1998, pp. 705-711 (in Japanese).

K.Higashikatsuragi, Y.Nakahata, I.Matsunami and A.Kajiwara, “Non-invasive respiration monitoring sensor using UWB-IR”, in Proc. IEEE ICUWB 2008, Hannover, German, Sept. 2008, pp.101-104.

N. Nakai, M. Watanabe, Y. Miyake, K. Takeda, K. Yamashita, H. Shinmori and K. Ishihara, “Automatic respiration monitoring system by time-varying image analysis,” IEICE Trans. Inf. & Syst., vol.J83-D-II, no.1, Jan. 2000, pp.280-288 (in Japanese).

K. Abbas, K. Heiman, T. Orlikowsky, S. Leonhardt. “Non-contact respiratory monitoring based on real-time IR-thermography,” in IFMBE Proc. of WC2009, 25/IV, 2009, pp.1306-1309.

J. Fei and I. Pavlidis, “Analysis of breathing air flow patterns in thermal imaging,” in Proc. IEEE EMBC 2006, New York City, NY, Aug. 2006, pp.946-952.

D. Hanawa, Y. Yaginuma, Y. Enomoto, T. Koide, S. Terada and K. Oguchi, “Automation of non-intrusive nasal breathing detection by using far-infrared imaging,” in Proc. u-Healthcare 2010,Nov. 2010.

D. Hanawa, T. Koide and K. Oguchi, “A proposal of nasal breathing detection method by using far infrared imaging in a home health care system,” IEICE Trans. Inf. & Syst., vol.J94-D, no.1, Jan. 2011, pp.260-263, (in Japanese).

T. Koide, S. Yamakawa, D. Hanawa and K. Oguchi, “Breathing detection by far infrared (FIR) imaging in a home health care system,” in Proc. IEEE ISBB 2009 [USB-Memory], Melbourne, Australia, Sept. 2009, pp. 206–209.

Z. Zhu, J. Fei and I. Pavlidis, “Tracking human breath in infrared imaging,” in Proc. IEEE BIBE 2005, Minnesota, USA, Oct. 2005, pp.227-231.

F.Q. AL-Khalidi, R. Saatchi, D. Burke and H. Elphick, “Tracking human face features in thermal images for respiration monitoring,” in Proc. IEEE/ACS AICCSA 2010, Hammamet, Tunisia, May 2010.

B. Kaur, J. K. Nelson, T. Williams and B. O’Kane, “Adaptive region of interest (ROI) detection and tracking for respiration measurement in thermal video,” in Proc. SPIE, Vol.8401, May 2012, pp.840117-1-84117-9

P. Viola and M. Jones, “Robust real-time face detection,” Int. J. Compt. Vison,vol.57,no.2,May 2004, pp.137-154.

R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” in Proc. ICIP 2002, Rochester, New York City, NY, Sept. 2002, pp.900-903.



  • There are currently no refbacks.