Thesis Presentation

Measurement of pulse rate of a person using his video

By Sahil ShahDate: 30-11-2012

Initial work•Literature Review: From literature we know that approaches have been found to extract human pulse information from the video of a stationary person.•One of the methods is using the mean values of the R,G,B streams from a specific region of interest of the face and plotting them over time from the video. •Analysis using Matlab.

Analysis

Time

Mean P

ixel Valu

e

Regions of Interest

Analysis

Frequency

Pow

er

The Power spectrum of the mean values signals for the RGB streams.Peak for the green signal can be seen at 1.2 Hz.

Process

Video Face Detection

ROI selection

Interpolation of RGB

values

Normalize Intensity

Processed Signals

Hann Windowin

g

Bayes Filter

Independent

Component

Analysis

Raw RGB signal

Fast Fourier

Transform

Processed Signals

Ideal Bandpass Filtering

Peak Detection

Pulse Parabola Estimation

Face Tracking•Two approaches:

1. Object tracking: We use the standard object tracking implementation in MIRA to detect the face. The ROIs are stated in the configuration file of the Pulse Detector unit as sub regions of the face. We select the largest detected object as the face and subsequently select the closest object to the last detection as the face.

ADV: • Faster• GeneralizedDIS: • Breaks when first detection is wrong (generally when face takes smaller area in the image)• ‘jumping’ detections.

Face Tracking2. Active Appearance Model (AAM): We use the

active appearance model algorithm to recognize faces based on multiple features. It returns triangles that define different features on the face. We configure the AAM face detector to return some pre selected triangles as ROIs

ADV: • More robust to small movements • Exact ROIs

DIS: • No generalized model for all kinds of faces

Getting R, G, B signals•Average R,G and B pixel values of the regions of interest from the face for each timestamp

•Interpolation to get RGB values for the timestamps for which we get images (since detections come little later)

•Sampling rate can be changed and is not required to be same as that of images because interpolation can also be used to get intensity values for any timestamp

•Interpolation also helps to maintain equal intervals between frames and increase accuracy

Processing R, G, B signals•Intensity Normalization:

rn = r/(r+g+b)gn = g/(r+g+b)bn = b/(r+g+b)

•Independent Component Analysis

•Hann Window: Reduces resolution but works better when S/R is low.

•Bayes Filtering: Kernel with +/-1 bin change (+/- 3 bpm for a window of 200 frames at 10Hz).

Extracting pulse•Fast Fourier Transform: Discrete Fourier transforms of the processed signals to get their power spectrum

•Band-pass filter: Band-pass filter (0.75 to 1.5) to get the frequency spectrum for the range in which the human pulse can lie.

•Peak Detection: Detects maximum power frequency

•Parabola estimation

•Calculate Pulse

Parameters•The Pulse Detector can be configured with the help of various parameters like:

Number of framesVirtual Sampling FrequencyRegions of InterestUse AAMUse ICABayes FilterWindowing (Hann)Filter BandsParabola Estimation

Evaluation•We evaluated the Pulse Detector Unit on the following factors

Motion vs StationaryAAM vs Object TrackingNear vs Far (Resolution)Jumping detections vs. Non jumping detectionDifferent ROIsICA vs No ICA

ResultsScreenshots

Implementation•The analysis and testing was done in Matlab while the entire implementation is in C++ using the Middleware for Robotic Applications (MIRA) framework.

Questions Addressed

Which algorithm is the most promising for usage?• The Object Tracking algorithm is giving better results

currently. • The AAM tends to lose the detections on increasing

movement.• But a better trained AAM will be more robust because it is

more accurate and gives the exact ROI thus effect of small noise becomes negligible.

What is the maximum distance of people in the image from where

robust pulse extraction is possible?• For stationary images taken using the Kinect sensor we

got good results even for face size 107x107 pixels from a 640x480 image.

• This was around 80 cm from the camera.

To what degree the people can move in the image without losing

pulse observation?• A well trained AAM would almost nullify the noise effects,

currently face tracking however is not so robust to higher noise (>10 pixels) specially when the person is farther from the camera.

What is the minimum duration of a video sequence to allow pulse

rate extraction?• 20 second blocks of video are sufficient for pulse rate

extraction. We take 20 second sliding window continuously for as long as the video is captured.

References[1] Remote plethysmographic imaging using ambient light. Verkruysse, W. and Svaasand, L.O. and Nelson, J.S., Optics express, nr. 26, vol. 16, pp. 21434-21445, Optical Society of America, 2008

[2] Eulerian video magnification for revealing subtle changes in the world. Wu, H.Y. and Rubinstein, M. and Shih, E. and Guttag, J. and Durand, F. and Freeman, W., ACM Transactions on Graphics (TOG), nr. 4, vol. 31, pp. 65, ACM, 2012

[3] Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Poh, M.Z. and McDuff, D.J. and Picard, R.W., Optics Express, nr. 10, vol. 18, pp. 10762-10774, Optical Society of America, 2010

Thank You!