## 5 Discussion
In all three conditions, characteristic performance issues were found in the camera-measured FAC data: erratically changing values containing frequency content associated with facial movements faster than the fastest possible microexpression [71]; values between zero and one, which corresponds to an impossible score within [PERSON]'s coding method [46]; values of lower intensity than the actions demonstrated by the model in the instructional film. However, it is unclear if the values between zero and one are due to some undocumented adaptation of the coding method, or a performance issue. Moreover, it is unclear if this underestimation is due to participants' task performance (high-intensity actions may be harder to reproduce) or system performance (_OpenFace_ 2 underestimates the magnitude of high-intensity facial actions). To avoid these issues influencing the outcomes of psychophysiological research, the recommendation is made to avoid drawing inferences based on microexpressions or low-intensity expressions, between zero and one, when using a similar camera-based FAC system. It should be noted that there is no conclusive indication that these characteristic errors were more prevalent with each incrementally darker room illumination, based on the analyses made. The analyses of the data collected suggest that the camera-based heart rate measurement solution implemented performs better under brighter room illumination. Notably, the difference in MAE between the setting was large and highly significant and the performance of the system was very poor in all but the bright condition. Hence, when using a similar camera-based HR measurement system, we strongly suggest the recreation of illumination conditions similar to the bright setting. Or if possible, brighter and with a smaller component of light emanating from the screen. The residual error between the camera-measured signal and the expected signal from the contact sensor can be partly characterised by distinct increases and decreases in heart rate occurring over a short time period. These upwards _spikes_ appear to be part of a wider trend of higher heart rate predictions being made by the camera-based system, compared to the contact sensor. These _spikes_ also contributed to the poor correlation between the camera-based heart rate measurements and the _Polar H10_ measurement. Even in the bright condition, the correlation was found to be weak (mean PCC = 0.28). Hence, the suggestion is made that in psychophysiological research, features relating to short-term changes in heart rate (e.g, once every second) should not be obtained using a similar camera-based HR measurement system. Instead, the recommendation is made to obtain sparse measurements over a longer duration, reducing the temporal resolution of the signal. For example, before, during, and after the viewing of screen-based stimuli, or once every minute. To achieve this, adaptations to the stride and window length settings within the _PyVHR_ configuration should be considered. This reduction in error, associated with an extended window length in depth, is explored in depth by [PERSON] et al. (2018). Occlusion events were identified as a source of erroneous measurements for both the camera-based HR measurement and FAC systems. When a face is not recognized, which can happen due to low illumination or obstructions, no measurement can be obtained. In our study, data, it is evident that the initial illumination during the instructional film contributed to such occurrences in the dark condition (refer to Figure 15). While no occlusion events were observed during the facial actions task of the instructional film, the decision to exclude data analysis from the head movement and eye movement tasks, as mentioned in Section 3.1, was made due to the prevalence of facial occlusion events. Specifically, rotating the head to look away from the screen, in any direction, was found to cause a loss of tracking.