## 2 Video Camera Imaging Based Method
### Colour-Based Method
From the frame sequences of the video captured by a camera, the PPG signal can be extracted by image and signal processing techniques. Over the past several years, numerous techniques in iPPG have been proposed under well controlled situations such as considering stationary subjects and stable illumination, as listed in Table 1. [PERSON] et al. [68] used normal ambient light as the light source to measure the HR and RR from the human face using a digital camera. In their proposed method, the ROI was selected manually, and the raw PPG signal was calculated per frame by an intensity method: averaging the spatial pixel value of the red, green, blue (RGB) colour channels. The fast Fourier transform (FFT) algorithm was used to calculate the power spectral density of the signal to extract HR. They showed that under these circumstances, the green channel with highest signal to noise ratio (SNR) contains the strongest plethysmography signal because oxygenated haemoglobin absorbs green light more than blue and red. In addition to HR and RR monitoring, they showed that PPG imaging can be used to characterise regions of high and low pulsatility on facial port wine stains (PWS). They used amplitude and phase maps to show the difference between normal skin and PWS skin. In normal skin, HR pulse amplitudes (G channel) were typically 2 to 4 times higher than in adjacent PWS skin, e.g., 0.75 and 0.25 PV, respectively. However, this method is strongly affected by motion artefacts caused by the subject's movement, as they did not consider methods for noise removal. Their ROI selection was manual. Figure 6: Skin reflection model of imaging photoplethysmography (iPPG) [adapted from [67]]. To overcome the limitations of the previous technique, [PERSON] et al. [69] proposed a novel method to measure HR, considering blind source separation (BSS) using a webcam and considering three subjects at a time. Moreover, they used the OpenCV face detection algorithm which is automatic and based on the Viola and Jones (V-J) [52] method. The facial ROI was defined as a rectangular bounding box. However, the proposed approach only considered very small movements and did not consider illumination variations. Additionally, [PERSON] et al. considered the second component produced by ICA as the PPG signal always. Moreover, they only measured HR in that study, a drawback that was minimised in [70] where they presented an enhanced method to measure RR and heart rate variability (HRV) along with HR. Another ICA based method was introduced by [PERSON] et al. [71] where they used two algorithms for extracting the pulse rate--a peak detection algorithm and power-spectrum analysis algorithm under both relaxed and active conditions--and found that power-spectrum performed better than peak detection. However, this method is highly susceptible to noise artefacts caused by illumination variations, subject movement and facial expressions. [PERSON] et al. [72] proposed another method based on BSS using principle component analysis (PCA) and reported that PCA is faster than ICA in terms of computation time and it can be a good alternative if only HR is extracted. However, in this method, only the stationary case and HR were considered.