## 5 Discussion  
In the methodology section, concerns were highlighted as to whether the relationship between the camera-measured signal (generated by _OpenFace 2_), and the expected signal (associated with perfectly replicating the model's movements) could be used to effectively assess system performance. Specifically, the concern was that the individual's improving performance in the facial action replication task, with each subsequent trial, may create a confounding effect between the illumination conditions which may lead to incorrect  
Figure 16: Example comparing the camera-measured signal and the physical heart rate sensor for a video captured in the bright illumination condition. Note the spiking behaviour in the camera-measured data. Figure 17: Two-dimensional histogram showing the relationship between the camera-measured heart rate values and the expected values as measured by the contact sensor, for each sample under each illumination condition. The dotted line describes where the two measures are equal. A bin size of ten helps characterise the relationship between the two variables clearly. conclusions to be drawn from the study. Therefore, any finding of a between illuminations effect was considered to be based on an assumption that participants perform the facial actions task consistently between trials. To test this assumption a repeated measures ANOVA was used, showing there is no such difference in MAE or PCC between trials. Hence, we consider that under the conditions of this study, this assumption has been met and that the findings regarding the influence of illumination on the performance of the camera-based FAC solution can be considered conclusive. To reduce any influence of inconsistent task performance, counterbalancing of the order of conditions should be applied and further considerations should be made to mitigate the influence of participants struggling to create certain facial actions (e.g., AU02). For example, the instructional film may be individualised to only prompt facial movements which participants can effectively recreate. Such optimisations could lead to an instructional film approach being a viable alternative to manually coded facial action datasets, for training and benchmarking camera-based FAC systems. In the case of this study, the results obtained should not be considered a comparable benchmark when compared to other datasets due to the influence of task performance on the MAE and PCC metrics. As the pacing of the instructional film was considered optimal by all but one of the participants, similar timings should be used in future research when applying this methodology. Another limitation of this research relates to the diversity of participants, given that 13 (65%) of the sample were white males. Hence, this demographic is over-represented. However, some interindividual factors which may hypothetically influence the performance of remote camera-based physiological measurement are well represented within the datasets. For example, many of the participants were wearing glasses (55%) or had facial hair (35%). These factors likely influenced the performance of the camera-based physiological measurement solution. However, they are not explored in this publication due to scope limitations. In future work exploring the use of camera-based physiological solutions, researchers should strive to obtain a more varied sample of participants. Analyses of the data collected suggest that the camera-based automatic FAC solution implemented performs better under brighter room illumination, as hypothesized. Although, the difference between the performance metrics for the medium and bright conditions is not considered statistically significant. Based on these results, we recommend that when using a similar camera-based FAC system, the room illumination should be at least as bright as the medium condition and should be kept consistent between participants.