Depression Detection Using Stacked Autoencoder From Facial Features And NLP


  • Ravi Kumar MTech Scholar Department of CSE NIIST, Bhopal, India
  • Santosh Kumar Nagar Assistant Professor Department of CSE NIIST, Bhopal, India
  • Anurag Shrivastava Associate Professor Department of CSE NIIST, Bhopal, India



Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods.


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How to Cite

Kumar, R., Nagar, S. K., & Shrivastava, A. (2020). Depression Detection Using Stacked Autoencoder From Facial Features And NLP. IJOSTHE, 7(1), 7.