Heart Rate Detection using Video Magnification: Impact of Algorithmic Parameters and Noise

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Yassin Kassab, Leen




Screening people for illnesses in public spaces using contactless techniques such as Video Magnification (VM) can be beneficial for both the crowd and the operators. One important vital sign to screen for is heart rate because it is indicative of the overall health of humans. In this thesis, first the impact of varying two input parameters (region of interest (ROI) size and window segment length) on VM is explored. Second, the impact of noise from three different sources (quantization, modern camera systems coupled with software enhancements, and light illumination) is explored. Results show that heart rate can be detected from very small ROIs, but that larger ROIs have higher accuracy. Larger window segment lengths are useful in the presence of motion. VM detects signals with amplitudes equal to one quantization level in 8-bit videos. VM is affected by modern camera systems, and it is more accurate with higher light illuminations.


Heart rate monitoring




Carleton University

Thesis Degree Name: 

Master of Applied Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).