  Human heartbeat can be measured using several different ways appropriately
based on the patient condition which includes contact base such as measured by
using instruments and non-contact base such as computer vision assisted
techniques. Non-contact based approached are getting popular due to those
techniques are capable of mitigating some of the limitations of contact-based
techniques especially in clinical section. However, existing vision guided
approaches are not able to prove high accurate result due to various reason
such as the property of camera, illumination changes, skin tones in face image,
etc. We propose a technique that uses video as an input and returns pulse rate
in output. Initially, key point detection is carried out on two facial
subregions: forehead and nose-mouth. After removing unstable features, the
temporal filtering is applied to isolate frequencies of interest. Then four
component analysis methods are employed in order to distinguish the
cardiovascular pulse signal from extraneous noise caused by respiration,
vestibular activity and other changes in facial expression. Afterwards,
proposed peak detection technique is applied for each component which extracted
from one of the four different component selection algorithms. This will enable
to locate the positions of peaks in each component. Proposed automatic
components selection technique is employed in order to select an optimal
component which will be used to calculate the heartbeat. Finally, we conclude
with a comparison of four component analysis methods (PCA, FastICA, JADE,
SHIBBS), processing face video datasets of fifteen volunteers with verification
by an ECG/EKG Workstation as a ground truth.
