## 3 Different Aspects of Colour-Based Method
### Others  
Based on machine learning, [PERSON] et al. [118] presented a novel method to extract HR using a video camera where the PPG signal was recovered from either ICA or chrominance-based methods and may be enhanced by utilising the mid-level PPG based features exploiting support vector regression (SVR). However, they only considered the stationary case. Using a deep convolutional attention network (CAN), [PERSON] et al. [119] introduced the first end-to-end system named DeepPhys for video-based measurement of HR and BR. They proposed a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enabled robust measurement under varying lighting and significant head motions. Normalised frame difference was used as input motion representation. The network learnt spatial masks, that were shared between the models, and features important for recovering the BVP and respiration signals. The motion model and the appearance model were learnt jointly to find the best motion estimator and the best ROI detector simultaneously. They evaluated their method on three datasets of RGB videos and a dataset of IR videos. The proposed approach significantly outperformed prior state-of-the-art methods (CHROM, POS etc.) on both RGB and infrared video datasets and allowed spatio-temporal visualisation of physiological information in video. Moreover, the participant dependent vs. independent performance as well as the transfer learning results showed that the supervised method generalised to other people, skin types and illumination conditions. [PERSON] et al. [120] proposed the first end-to-end spatiotemporal network (PhysNet) to recover iPPG signals precisely from raw facial videos, considering a temporal context which was not considered in previous works. They conducted comprehensive experiments on OBF and MAHNOB-HCI datasets. First, the face area  
Figure 11: RMSE performance comparison of CEEMDAN and CCA techniques under multiple detection [117]. **(a)** RMSE performance comparison for HR, **(b)** RMSE performance comparison for RR.  
Figure 12: RMSE performance comparison of CEEMDAN and CCA techniques at long range [117]. **(a)** RMSE performance comparison for HR, **(b)** RMSE performance comparison for RR.  
was detected using the V-J face detector. Then, to extract the iPPG signal, a spatio-temporal network with 3D convolutional neural networks (3 DCNN) and a recurrent neural network (RNN) was used. After that, filtering, normalisation, and peak detection were performed to attain the inter-beat-intervals. Finally, the average HR and HRV were calculated. Experimental results showed that the proposed PhysNet reconstructed iPPG signals with accurate time location of each individual pulse peak and achieved better performance on both HR and HRV levels compared to the state-of-the-art methods (CHROM, POS etc.). This method has potential applications in remote arterial fibrillation detection and emotion recognition.  
To integrate colour-based and motion-based approaches, [PERSON] et al. [121] introduced two fusion techniques, the mean method and the ratio of variations method, to extract HR using an industrial camera. They reported that a colour-based method is more reliable than a motion-based method. However, the accuracy of the fusion-based method was not satisfactory, and this method was confined to rehabilitation only.