Abstract
Emotional state analysis of facial expression is an important research content of emotion recognition. At the same time, in the medical field, the auxiliary early screening tools for depression are also urgently needed by clinics. Whether there are differences in facial expression changes between depressive patients and normal people in the same situation, and whether the characteristics can be obtained and recognized from the video images of depressive patients, so as to help doctors detect and diagnose potential depressive patients early are the contents of this study. In this paper, we introduce the videos collection process of depression patients and control group at Shandong Mental Health Center in China. The key facial features are extracted from the collected facial videos by person specific active appearance model. On the basis of locating facial features, we classified depression with the movement changes of eyes, eyebrows and corners of mouth by support vector machine. The results show that these features are effective for automatic classification of depression patients.
Published Version
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