Eyes Speak Louder: Harnessing Deep Features From Low-Cost Camera Video for Anxiety Detection

Social anxiety is a common mental health disorder, affecting approximately 36% of the world's population. Currently, diagnosing social anxiety disorder relies on clinical interviews and self-reported questionnaires, which are prone to subjective biases. Discovering non-intrusive, objective markers of anxiety, such as eye gaze patterns, could enhance current diagnostic approaches and facilitate early assessment, thereby reducing the treatment gap. Previous research has explored predicting anxiety states using physiological signals and audio data, but conclusive results remain elusive.