## 3 Different Aspects of Colour-Based Method
### Illumination Variations  
While most studies are concerned about the motion artefacts, other studies have considered how to mitigate illumination variations, as listed in Table 3. For example, [PERSON] et al. [89] proposed a new robust method to measure the pulse signal using a camera that is based on reflectance decomposition on the green channel and ensemble empirical mode decomposition (EEMD) to suppress the noise caused by illumination variation. First, videos covering the brow area were captured using a digital camera. Then, the green channel was selected for reflectance decomposition as oxygenated blood absorbs green light more than red and blue. Reflectance decomposition was done using the alternating direction method of multipliers (ADMMs). To remove noise caused by illumination variations, EEMD was used to decompose the original time signal from face reflectance to a set of intrinsic mode functions (IMFs). They selected IMF4 as it was closest to normal heart rate frequency. Finally, a peak detection algorithm was applied to detect the number of peaks and, finally, HR was calculated. The experimental result showed that their method outperformed [PERSON]'s method [69], providing better measurement accuracy with a smaller variance. This method was further improved in [90] where HR was evaluated by means of a multiple linear regression (MLR) model followed by Poisson distribution to reduce the effects of ambient light changes. Nevertheless, EEMD has limitations of inadvertently considering periodic illuminant variations as physiological signals, especially if the frequency is near the normal cardiac frequency range, particularly from 0.75 to 4 Hz. Moreover, both methods are not suitable for real time application. To measure HR, [PERSON] et al. [91] used a different approach based on multi order curve fitting (MOCF) to remove noise artefacts, considering subjects watching television in a dark room. In the proposed approach, they subtracted the estimated brightness signal from the raw PPG signal to cancel out the noise caused by illumination variations. Another study by [PERSON] et al. [92] presented a new technique based on autoregressive (AR) modelling and pole cancellation to cancel out the aliased frequency components caused by artificial light flicker, which enhanced robustness of measurement under strong fluorescent lights. Nevertheless, the periodic illumination variations may affect the performance as AR modelling is a spectral analysis technique. Moreover, there is a calibration issue while calculating oxygen saturation as well. To extract HR using a webcam, [PERSON] et al. [93] designed a robust method exploiting illumination variation by combining joint blind source separation (JBSS) and EEMD and considering background images as well. However, this method is not free from motion artefacts as they only considered stationary subjects. Moreover, the illumination variation considered here was mostly controlled, which restricts real time applicability of the proposed method. Another method that was robust to illumination variations was proposed by [PERSON] et al. [94], using partial least squares (PLS) and multivariate empirical mode decomposition (MEMD) to efficiently extract HR using a webcam under varying illumination conditions. However, they only considered artificial illumination created by an LED lamp.  
Most of the above-mentioned works are concerned about the elimination of the effect of illumination variations and keeping the participants still, which means that they did not consider motion artefacts. Nevertheless, both illumination variations and motion artefacts are important in realistic applications. So, these approaches which are initially considered to remove illumination variations should be further improved by considering motion artefacts.