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AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor.

The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those a...

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Publicado en:Medical & Biological Engineering & Computing Vol. 59; no. 6; pp. 1339 - 1355
Autores principales: Tadalagi, Manjunath, Joshi, Amit M.
Formato: Journal Article
Publicado: Springer Nature Jun2021
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Jun2021
      vid: 59
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      pub: Springer Nature
      place: New York, New York
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        NLM34091864
        10.1007/s11517-021-02358-2
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        atl: AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor.
      aug:
        au:
          Tadalagi, Manjunath
          Joshi, Amit M.
        affil: Malaviya National Institute of Technology, Jaipur, India
      sug:
        subj:
          Facial Expression
          Depression Diagnosis
          Algorithms
          Scales
      ab: The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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