• Sukrut Dani
• Pratiksha Nandre
• Shubham Pansare
• Mitali Yeware
• Mr. Pratik Kamble

Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self-questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the facial expressions and voice recognition. Some queries are raised that which features are more responsible for depression from image and voice and which classifiers gives good results. By identifying proper features from image and voice recognition system even one can save the life of a patient. A system has been proposed which captures frontal face videos of patients, extracts the facial features from each frame and analyses these facial features to detect signs of depression in them. This system will be trained with of frontal face images of happy, sad and normal faces. The presence of these features in the video frames will be analyzed to predict depression in the patients. The most important form of nonverbal communications is facial expressions of a person. This system mainly uses different image processing techniques for face detection, feature extraction and classification of these features as depressed or non-depressed. Based on the level of depression features the student will be classified as depressed or not. Along with detecting depression from facial expressions a voice detection system is also used simultaneously for efficient working of the overall system. Voice recognition system carries crucial information about recognition and translation of spoken language into text by computers.

"Depression Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.434-438, May-2019, Available :http://www.jetir.org/papers/JETIR1905N64.pdf

"Depression Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp434-438, May-2019, Available at : http://www.jetir.org/papers/JETIR1905N64.pdf