TY - GEN
T1 - RealSense = real heart rate
T2 - 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016
AU - Chen, Jie
AU - Chang, Zhuoqing
AU - Qiu, Qiang
AU - Li, Xiaobai
AU - Sapiro, Guillermo
AU - Bronstein, Alex
AU - Pietikäinen, Matti
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - Recent studies validated the feasibility of estimating heart rate from human faces in RGB video. However, test subjects are often recorded under controlled conditions, as illumination variations significantly affect the RGB-based heart rate estimation accuracy. Intel newly-announced low-cost RealSense 3D (RGBD) camera is becoming ubiquitous in laptops and mobile devices starting this year, opening the door to new and more robust computer vision. RealSense cameras produce RGB images with extra depth information inferred from a latent near-infrared (NIR) channel. In this paper, we experimentally demonstrate, for the first time, that heart rate can be reliably estimated from RealSense near-infrared images. This enables illumination invariant heart rate estimation, extending the heart rate from video feasibility to low-light applications, such as night driving. With the (coming) ubiquitous presence of RealSense devices, the proposed method not only utilizes its near-infrared channel, designed originally to be hidden from consumers; but also exploits the associated depth information for improved robustness to head pose.
AB - Recent studies validated the feasibility of estimating heart rate from human faces in RGB video. However, test subjects are often recorded under controlled conditions, as illumination variations significantly affect the RGB-based heart rate estimation accuracy. Intel newly-announced low-cost RealSense 3D (RGBD) camera is becoming ubiquitous in laptops and mobile devices starting this year, opening the door to new and more robust computer vision. RealSense cameras produce RGB images with extra depth information inferred from a latent near-infrared (NIR) channel. In this paper, we experimentally demonstrate, for the first time, that heart rate can be reliably estimated from RealSense near-infrared images. This enables illumination invariant heart rate estimation, extending the heart rate from video feasibility to low-light applications, such as night driving. With the (coming) ubiquitous presence of RealSense devices, the proposed method not only utilizes its near-infrared channel, designed originally to be hidden from consumers; but also exploits the associated depth information for improved robustness to head pose.
KW - Heart Rate Estimation
KW - Illumination Invariant
KW - RealSense
UR - http://www.scopus.com/inward/record.url?scp=85013176258&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013176258&partnerID=8YFLogxK
U2 - 10.1109/IPTA.2016.7820970
DO - 10.1109/IPTA.2016.7820970
M3 - Conference contribution
AN - SCOPUS:85013176258
T3 - 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016
BT - 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016
A2 - Pietikainen, Matti
A2 - Hadid, Abdenour
A2 - Lopez, Miguel Bordallo
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2016 through 15 December 2016
ER -