International Journal of Scientific Research and Engineering Development(http://www.ijsred.com)

1. International Journal of Scientific Research and Engineering Development-– Volume 2 Issue 5, September 2019
Available at www.ijsred.com
ISSN : 2581-7175 ©IJSRED: All Rights are Reserved Page 470
Depression Analysis Using Image Processing and Machine
Learning
Abhijit Pawar*, Smiral Bandal**, Rushikesh Borate***, Pooja Jadhav****, Sneha
Panjabi*****
*(Department of Information Technology, SPPU/ SVPM College Of Engineering , Malegaon bk)
**(Department of Information Technology, SPPU/ SVPM College Of Engineering , Malegaon bk)
***(Department of Information Technology, SPPU/ SVPM College Of Engineering , Malegaon bk)
****(Department of Information Technology, SPPU/ SVPM College Of Engineering , Malegaon bk)
*****(Department of Information Technology, SPPU/ SVPM College Of Engineering , Malegaon bk)
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Abstract:
Human emotions are an important part of our life. We express our feelings through emotions.
When a person is sad, then the person cause a stress. But when that stress prolongs more than a week then
such a condition called as a “Depression”. Depression is caused due to the people live at fast life structure
and cause pressure in their workplace, family etc. Depression is one type of mental illness. Depression is a
common mental illness with a prevalence of 10 to 15% in population. Now it seems to be a common type
of problem in most of the person. Hence to detect the depression state in human being, there is need to
develop some tools which can detect the depression in human. This survey paper describes different
algorithms and methods which will help to detect the depression phase in human by face recognition. This
paper specifically gives the methods for recognition of face, classification techniques for detection of
different face parts like eyes, lips etc. By using this techniques and algorithm one can collect the data base
for depression analysis. Consequently with the assistance of strategies and calculations portrayed in paper,
it is anything but difficult to recognize the downturn without the physical nearness of specialists.. Also
papers describes the automatic depression detection in adolescents.
Keywords — Face recognition, feature extraction, compare dataset, machine learning, image
processing .
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I. INTRODUCTION
The present work is an orderly survey of existing
strategies for programmed identification and
additionally seriousness appraisal of gloom.
Accentuation is offered to methodologies using
visual hints from the picture preparing and ai
viewpoint trying to fill the hole of past exhaustive
surveys. The point of the audit is to look at
strategies for computerized sadness investigation,
which could help clinicians in the analysis and
observing of misery.
Discouragement examination framework is take a
shot at picture handling. Right off the bat, picture
recognization is done at that point highlight
extricated. Picture is changed over into dark scale
picture. This is yield for highlight choice at that
point testing dataset is contrast and preparing
informational index lastly yield is created utilizing
mark.
RESEARCH ARTICLE OPEN ACCESS

2. International Journal of Scientific Research and Engineering Development-– Volume 2 Issue 5, September 2019
Available at www.ijsred.com
ISSN : 2581-7175 ©IJSRED: All Rights are Reserved Page 471
In human-to-human discussion, the verbalization
and impression of outward appearances structure a
correspondence direct notwithstanding voice which
conveys indispensable data about the psychological,
passionate, and even physical condition of the
people in discussion. An individual's outward
appearances in its most straightforward structure is
a progressively inconspicuous cheerful or furious
musings, emotions or comprehension of the speaker
expected or unforeseen reaction from audience
members, compassion, or even what the speaker is
stating no sign can give to processing foundation,
carries our ordinary human client to stay at the front
line in the texture will move to retain. This set up a
by and large forecast, inescapable registering and
encompassing insight, for example, expected to
accomplish the up and coming age of processing.
It's anything but difficult to normally happening
multimodal human-human correspondence centered
reaction to the ui should be created to distinguish
such interfaces and aims and as communicated by
sentiments of social and enthusiastic markers
should have the capacity. This vision of things to
come rouses the exploration for computerized
acknowledgment of nonverbal activities and
articulation.
I. Project Goals And Objectives
GOALS
Research on programmed wretchedness evaluation
has made some amazing progress from Cohn et al.
also, McIntyre et al. with a few novel
methodologies both as far as philosophy and
execution. The present complete survey of the best
in class gives various bits of knowledge, while
distinguishing numerous inquiries open to assist
examination. Gloom finding itself is a functioning
and dubious theme in clinical brain research and
psychiatry. Given the previously mentioned
remarkable issues, the improvement of
computerized, target appraisal strategies might be
significant for both research and clinical practise.
OBJECTIVES
• The face image captured is now used in the
face recognition process.
• This face image is analyzed and considered
as a high-dimensional vector.
• This image value is then compared to all the
face images in the database, looking for a
match.
• Face plays a big role in conveying identity
and emotion, being the primary focus of
attention in social life.
• Computational model off ace recognition
can be applied to criminal identification,
security systems, image and film processing
and human computer interaction
II. LITERATURE SURVEY
1)The first literature is on the automatically
depression assess.In this paper they focuses on the
image processing and machine learning
algorithms.This system is assist clinicians in the
monitoring the the depression based on depression
assessment.
2)Second is the face recognition using the bunch
graph matching. In this the system is recognizing
the human faces from single image out of thev large
or big dataset containing one image per person. The
face is regnizing by labled.the image graph values
is extracting by on elastic graph matching process
and they can compared by single function.This
graph is construct for small set of sample image
graph.
III. METHODOLOGY
The main purpose of the system is to recognize
depression from an image and give the output
using label. The process methodology consist of
the following steps data set , preprocessing ,
feature extraction , face recognition, feature
selection, model training .Depression analysis
system which is used multiclass classifier here we
can classify the human depression for
classification we use data set having various
instances. After pre-processing depression

3. International Journal of Scientific Research and Engineering Development-– Volume 2 Issue 5, September 2019
Available at www.ijsred.com
ISSN : 2581-7175 ©IJSRED: All Rights are Reserved Page 472
analysis system is designed on training data
set,then for performance measurement distinct
data set is used.
Fig: Flow Digram
Methodologies for challenge to suggest people best
face expression:
My work presents an expression recognition
method by manifesting the facial characteristics
through false geodesic distance and remodeling the
facial expressions in the 3D space and verifies this
method being able to identify different expressions
from static facial images in a more effective and
remarkable manner. The algorithms demonstrate
that this method achieves better results of
expression recognition than traditional methods and
shows stronger robustness to changes of
illumination.Facial Expression can deal with the
studies related to psychology, driver state
surveillance, patient monitoring etc. In the
developed countries the hospitals use the facial
expression tool to study the emotional states of
patients who are deaf dumb or blind , or in many
cases the patients who are not able to explain their
emotional state due to injury, disease etc. This can
help the doctors to monitor them and regulate the
medications and proper care.
1.Application
Clinical Research
Business Intelligence
Healing Centers
IV. CONCLUSION
The depression analysis system focuses on
analyzing and monitoring human behavior. In this
paper we have performing to find out the person’s
depress or not. So that we can use this model for
future use according to the result. It comprises of
steps like data comparison ,feature extraction ,face
recognition ,testing and training data data set .
VI.REFERENCES
[1]A .Pampouchidou , K.Marias,
M.Tsiknakis, P.Simos and F.Yang
F.Meriaudeau “Designing a Framework for
Assisting Depression Severity Assessment
from Facial Image Analysis ”2015 IEEE
International Conference on Signal and
Image Processing Applications (ICSIPA)
[2]R. E. Roberts, et al., "Screening for
Adolescent Depression: A Comparison of
Depression Scales," Journal of the American
Academy of Child & Adolescent Psychiatry,
vol. 30, pp. 58-66, 2009.
[3]Jeffrey Cohn, Ellen Frank. Zara
Ambadar, Joan Buttenfield, KateJordan,
Javier Montano, Manuel Quero, and Nicki
Ridgeway “ detecting depression by facial
actions”IEEE 2009.
[4]M. Valstar, B. Schuller, K. Smith, T.
Almaev, F. Eyben, J. Krajewski, R. Cowie,
and M. Pantic, “Avec 2014: 3d dimensional

4. International Journal of Scientific Research and Engineering Development-– Volume 2 Issue 5, September 2019
Available at www.ijsred.com
ISSN : 2581-7175 ©IJSRED: All Rights are Reserved Page 473
affect and depression recognition
challenge,” in Proceedings of the 4th
International Workshop on Audio/Visual
Emotion Challenge. ACM, 2014, pp. 3–10.
[5]P. H. Waxer, “Therapist training in
nonverbal communication. I: Nonverbal
cues for depression,” Journal of Clinical
Psychology, vol. 30, no. 2, p. 215, 1974.
[6]G. Tzimiropoulos and M. Pantic, “Gauss-
newton deformable part models for face
alignment in-the-wild,” in IEEE Conference
on Computer Vision and PatternRecognition
(CVPR). IEEE, 2014, pp. 1851–1858