# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 2 Related Work
However, work related to real-time automatic gaze analysis to detect gaze for predicting SAD is scarce. Researchers like [PERSON] et al. [8] and [PERSON] et al. [18] suggested that SAD is associated with gaze avoidance. [PERSON] et al. [19] estimated gaze interaction in a live situation where women saw the videos of the confederate. The videos were pre-recorded, but the women participants had no idea about it. For them, it was live remote communication. However,one of the drawbacks of the study was the use of human coders to code gaze avoidance or gaze interaction. Such coding could be biased because of the human coder's involvement. Assuming a coder is not biased, there is still a possibility of human error due to loss of concentration, over-coding, or under-coding of the values. Such errors might result in a prediction error. [PERSON] et al. [33] measured the gaze interaction in a live communication where all participants took part in a role-play task, which was a conversation with an unknown female (whom they were told was also a study participant). Two video cameras were fixed on the wall of the test room. A stopwatch was used to figure out the total number of seconds in which the participants maintained gaze interaction with their confederates during each minute of the communication. The drawback of this study was again the involvement of a human who used a stopwatch to measure the time of gaze interaction or gaze avoidance. Hence, the same flaws, as explained in an earlier paragraph, might have crept into the study as well. [PERSON] et al. [18] measured gaze avoidance by setting up a live remote environment. The participants interacted with the interviewer through a webcam. However, [PERSON] et al. [8] argued that such a live remote environment setup might not be able to produce real anxiety situations in social environments as in live F2F communication and might not have produced the correct results. There are some other shortcomings suggested by [PERSON] et al. [34]. For example, the authors measured the gaze behavior in live communication, where participants talked with their real friends. A video from live communication was recorded. However, the problem is that talking to friends is what most of us do in normal daily life and thus might not be a trigger for SAD. Secondly, the coders used manual detection of gaze interaction while watching the video. As said earlier, the use of a manual technique to measure gaze interaction is not an efficient approach as human coders could miss an essential gaze interaction or avoidance moment. Furthermore, such coding might be prone to the coder's bias, who ultimately knew the purpose of the experiment. Therefore, there is a need for a technique to automate the measurement of gaze interaction in live communication to detect SAD. Table 1 summarizes the discussion provided in the related work section. The first column shows the reference from where the parameters are extracted. The second column shows the use of a live environment or not, while the third column explains whether manual or automated coding is used in the research. The fourth column explains whether the research compares the sub-classes of SAD with gaze interaction or not. The fifth column supplies information about the production of any public dataset. The last column states whether any relationship was proved between the SAD and gaze interaction in the research or not. The table highlight research gaps such as no usage of automatic prediction of gaze in a live environment. Furthermore, no study proved a relationship between gaze interaction/avoidance with SAD classes. In addition, the lack of a dataset in SAD prediction is also a gap that this study aims to fill in.