Designing a framework for assisting depression severity assessment from facial image analysis

Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis G.; Yang, Fan; Mériaudeau, Fabrice

doi:10.1109/icsipa.2015.7412257

Cited by 31 publications

(14 citation statements)

References 25 publications

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“…Finally, shaking and/or fidgeting behavior, self-adaptors, and foot tapping [102] have also been considered as signs of depression. Table 2, inspired by [64], [98], [103], [114] and enhanced hereby, is summarizing all of the signs and signals related to depression assessment as found in the literature to date.…”

Section: Nonverbal Signs For Depression Assessmentmentioning

confidence: 99%

Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

Pampouchidou

Simos

Marias

et al. 2019

IEEE Trans. Affective Comput.

151

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Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

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Section: Nonverbal Signs For Depression Assessmentmentioning

confidence: 99%

Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

Pampouchidou

Simos

Marias

et al. 2019

IEEE Trans. Affective Comput.

151

View full text Add to dashboard Cite

show abstract

“…for depression analysis. Data collection of depressed patients has also been done in [7] by indicating them film-clips to catch the outward appearances of feelings and furthermore by giving an assignment of perceiving negative and positive feelings from various facial pictures. In [8], for a video, the face region is first manually initialized and then KLT (Kanade-Tomasi-Lucas) tracker is utilized to extract curvature information from the picture.…”

Section: Introductionmentioning

confidence: 99%

A computer vision based image processing system for depression detection among students for counseling

Namboodiri

Venkataraman

2019

IJEECS

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Psychological problems in college students like depression, pessimism, eccentricity, anxiety etc. are caused principally due to the neglect of continuous monitoring of students’ psychological well-being. Identification of depression at college level is desirable so that it can be controlled by giving better counseling at the starting stage itself. The disturbed mental state of a student suffering from depression would be clearly evident in the student’s facial expressions.Identification of depression in large group of college students becomes a tedious task for an individual. But advances in the Image-Processing field have led to the development of effective systems, which prove capable of detecting emotions from facial images, in a much simpler way. Thus, we need an automated system that captures facial images of students and analyze them, for effective detection of depression. In the proposed system, an attempt is being made to make use of the Image processing techniques, to study the frontal face features of college students and predict depression. This automated system will be trained with facial features of positive and negative facial emotions. To predict depression, a video of the student is captured, from which the face of the student is extracted. Then using Gabor filters, the facial features are extracted. Classification of these facial features is done using SVM classifier. The level of depression is identified by calculating the amount of negative emotions present in the entire video. Based on the level of depression, notification is send to the class advisor, department counselor or university counselor, indicating the student’s disturbed mental state. The present system works with an accuracy of 64.38%. The paper concludes with the description of an extended architecture for depression detection as future work.

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“…[7] It has also been proposed to use these facial markers digitally for diagnostic and monitoring purposes through computer algorithms and software programs, fostering a new era of use of innovative technology in the medical field. [8]…”

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confidence: 99%