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© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 463
ANALYZING AND IDENTIFYING SOCIAL
NETWORK MENTAL DISORDERS VIA ONLINE
SOCIAL MEDIA MINING
Dr.S.G.Balakrishnan 1 AJAZ AHMAD HURRAH2
1 ASSOCIATE PROFESSOR, 2 PG STUDENT
COMPUTER SCIENCE AND ENGINEERING
MAHENDRA ENGINEERING COLLEGE (AUTONOMOUS), TAMILNADU, INDIA
ABSTRACT
The rapid growth of social networking has led to problematic usage. Today’s world solely depends on
Social media and due to excessive use, number of mental disorders like Cyber-Relationship Addiction,
Information Overload and Net Compulsion has been observed in online users. Symptoms of these mental
disorders are usually detected passively today, which results in delayed clinical intervention. As detecting these
SNMDs is challenging through users online activity. We propose a deep learning framework that exploits features
analyzed from the Social media data mainly images to identify potential SNMDs by classifying the emotion
features and expressions like happy, angry, sad, normal, surprise disgust etc., as positive and negative and
predicting the mental disorder accordingly. Image processing, Gabor filters with Convolution Neural Networks is
used for detection and prediction of expressions of face images. The proposed is expected to provide promising
results for identifying online social network users with potential Social Network Mental Disorders based on
expression of online user.
Keywords : SNMD & Social Edia Mining
1. INTRODUCTION
With the growth and popularity of social networking and messaging apps, online social networks (OSNs)
have become part and parcel of people’s lives. Most of the research done on social media network mining mostly
concentrates on discovering the knowledge behind the data for improving user’s life. Even though OSNs have
expanded peoples capability in increasing social contact but they have actually decreased face-to-face personal
interactions in real world due to this new terms like Phubbing and Nomophobia have been coined to define
people who are addicted to using social networking apps.
Research has been already done in the past to explore data mining techniques to detect various Social
Network Mental Disorders like:
1) CYBER-RELATIONSHIP (CR) ADDICTION, which includes the addiction to social networking, checking
and messaging to the point where social relationships to virtual and online friends become more important than
real-life ones with friends and families;
2) NET COMPULSION (NC), which includes compulsive online social gaming or gambling, often resulting in
financial and job-related problems; and
3) INFORMATION OVERLOAD (IO), which includes addictive surfing of user status and news feeds, leading
to lower work productivity and fewer social interactions with families and friends offline.
In the existing research feature analysis is performed on large data sets by feeding the data of users to
machine learning framework namely Social Network Mental Disorders Detection to identify the various mental
disorders. Multimedia data like images where not considered for this analysis. In this work we will be analyzing
online images of users and perform emotions analysis using image processing and using Gabor filters and
Convolutional Neural Networks (CNN) to predict whether person is suffering from social disorder as we will be
able to determine if person is happy, hurt, sad, scared, surprised, furious etc.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 464
2. SOFTWARE TESTING INTRODUCTION
Software testing is a process used to help identify the correctness, completeness and quality of developed
computer software. Software testing is the process used to measure the quality of developed software .Testing is
the process of executing a program with the intent of finding errors. Software testing is often referred to as
verification & validation.
Explanation for SDLC & STLC
SDLC: The software development life cycle (SDLC) is a conceptual model used in project management
that describes the stages involved in an information system development project, from an initial feasibility study
through maintenance of the completed application.
2.1 PHASES OF SOFTWARE DEVELOPMENT:
REQUIREMENT ANALYSIS
The requirements of a desired software product are extracted. Based the business scenario the SRS (Software
Requirement Specification) document is prepared in this phase.
Design Plans are laid out concerning the physical construction, hardware, operating systems, programming,
communications, and security issues for the software. Design phase is concerned with making sure the software
system will meet the requirements of the product.
THERE ARE 2 STAGES IN DESIGN,
HLD – High Level Design
LLD – Low Level Design
HLD – gives the architecture of the software product to be developed and is done by architects and senior
developers.
LLD – done by senior developers. It describes how each and every feature in the product should work and how
every component should work. Here, only the design will be there and not the code.
TESTING Testing is evaluating the software to check for the user requirements. Here the software is evaluated with intent of
finding defects.
MAINTENANCE once the new system is up and running for a while, it should be exhaustively evaluated. Maintenance must be
kept up rigorously at all times. Users of the system should be kept up-to-date concerning the latest modifications
and procedures.
2.2 SDLC MODELS
WATER FALL MODEL
It will be executing one by one of the SDLC process. The design Starts after completing the requirements
analysis coding begins after design. It is a traditional model It is a sequential design process, often used in SDLC,
in which the progress is seen as flowing steadily downwards ( like a waterfall ), through the different phases.
PROTOTYPE MODEL
Developed from the sample after getting good feedback from the customer. This is the Valuable mechanism for
gaining better understanding of the customer needs.
RAPID APPLICATION DEVELOPMENT MODEL (RAD):
This mechanism will develop from already existing one .If the new requirement is matching in already existing
requirement, will develop from that.
SPIRAL MODEL:
This mechanism is updating the application version by version. All the SDLC process will update version by
version.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 465
V-MODELV: V model is a process where the development and testing phases can do parallel. For every development phase
there is a testing phase. Development phases are called as verification whereas testing phases are called as
validation
STLC (SOFTWARE TESTING LIFE CYCLE): Testing itself has many phases i.e. is called as STLC. STLC is part of SDLC and includes following:
2.3 TEST PLAN
It is a document which describes the testing environment, purpose, scope, objectives, test strategy, schedules,
mile stones, testing tool, roles and responsibilities, risks, training, staffing and who is going to test the application, what type of tests should be performed and how it will track the defects.
TEST DEVELOPMENT
Preparing test cases, test data, Preparing test procedure, Preparing test scenario, Writing test script
TEST EXECUTION
In this phase we execute the documents those are prepared in test development phase
ANALYZE RESULT:
Once executed documents will get results either pass or fail. we need to analyze the results during this phase.
DEFECT TRACKING:
Whenever we get defect on the application we need to prepare the bug report file and forwards to Test Team Lead
and Dev Team. The Dev Team will fix the bug. Again we have to test the application. This cycle repeats till we
get the software without defects.
TYPES OF TESTING:
Following are the types of testing:
WHITE BOX TESTING
White box testing as the name suggests gives the internal view of the software. This type of testing is also known
as structural testing or glass box testing as well, as the interest lies in what lies inside the box.
BLACK BOX TESTING
It’s also called as behavioral testing. It focuses on the functional requirements of the software. Testing either
functional or non-functional without reference to the internal structure of the component or system is called black
box testing.
GREY BOX TESTING
Grey box testing is the combination n of black box and white box testing. Intention of this testing is to find out
defects related to bad design or bad implementation of the system.
COMPONENT TESTING
Instatement checking is the primary degree of effective checking and is primary duty of designers and after that
of the testing engineers. component checking is done following the normal check outcomes are satisfied else
contrasts are logical/adequate.
INTEGRATION TESTING
All modules that do application are tried. Indulge checking the communication of minimum 2 segments produces
outcomes that fulfill practical prerequisite.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 466
3.1 SYSTEM-TESTING
To test the total framework as far as usefulness and non-usefulness. It is black box testing, performed by the
Test Team, and toward the beginning of the framework testing the total framework is designed in a controlled
domain. To check the absolute system to the extent helpfulness and non-value. It is discovery trying,
implemented by the check Team, and around the start of the structure testing the all-out system is planned in a
controlled space.
3.2 FUNCTIONAL TESTING
The dynamic associations from all of the pages from unequivocal territory under test. Test each and
every inside association. Test associations jumping on comparative pages. Check for the default estimations of fields. Wrong contributions to the fields in the structures.
3.3 ALPHA-TESTING
Alpha-testing is last attempting before the item is released to the general populace. This testing is aimed
at the architect site and in a supervised circumstance by the last customer of the item.
3.4 BETA-TESTING
The beta-test is aimed in any event one customer areas by the end customer of the item. The beta-test is
driven at any rate one customer areas by the last customer of the item.
3.5 COMPONENT TESTING CASES
Presentation testing is the chief degree of dynamic checking and is first the commitment of originators
and subsequently that of the test engineers. Unit testing is performed after the typical test results are met or
differentiations are sensible/attractive.
A result is the last after effect of exercises or events conveyed abstractly or quantitatively. Execution
examination is an operational assessment, is a ton of fundamental quantitative association between the
introduction sums.
4. PROJECT METHODOLOGY
It will cover the details explanation of methodology that is being used to make this project complete and
working well. Many methodology or findings from this field mainly generated into journal for others to take
advantages and improve as upcoming studies. The method is use to achieve the objective of the project that will
accomplish a perfect result. In order to evaluate this project, the methodology based on System Development Life
Cycle (SDLC), generally three major step, which is planning, implementing and analysis.
Figure 1: Software Development Life Cycle
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 467
Figure 2: Steps of Methodology
4.1 LANNING:
To identify all the information and requirement such as hardware and software, planning must be done in the
proper manner. The planning phase has two main elements namely data collection and the requirements of
hardware and software.
DATA COLLECTION:
Machine learning needs two things to work, data (lots of it) and models. When acquiring the data, be sure
to have enough features (aspect of data that can help for a prediction, like the surface of the house to predict its
price) populated to train correctly your learning model. In general, the more data you have the better so make to
come with enough rows. The primary data collected from the online sources remains in the raw form of
statements, digits and qualitative terms. The raw data contains error, omissions and inconsistencies. It requires
corrections after careful scrutinizing the completed questionnaires. The following steps are involved in the
processing of primary data. A huge volume of raw data collected through field survey needs to be grouped for
similar details of individual responses.
Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other
words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for
the analysis. Therefore, certain steps are executed to convert the data into a small clean data set. This technique is
performed before the execution of Iterative Analysis. The set of steps is known as Data Preprocessing. It includes
-
Data Cleaning
Data Integration
Data Transformation
Data Reduction
Data Preprocessing is necessary because of the presence of unformatted real-world data. Mostly real-
world data is composed of -
Inaccurate data (missing data) - There are many reasons for missing data such as data is not continuously
collected, a mistake in data entry, technical problems with biometrics and much more.
The presence of noisy data (erroneous data and outliers) - The reasons for the existence of noisy data
could be a technological problem of gadget that gathers data, a human mistake during data entry and much more.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 468
Inconsistent data - The presence of inconsistencies are due to the reasons such that existence of
duplication within data, human data entry, containing mistakes in codes or names, i.e., violation of data
constraints and much more.
In this final phase, we will test our classification model on our prepared image dataset and also measure
the performance on our dataset. To evaluate the performance of our created classification and make it comparable
to current approaches, we use accuracy to measure the effectiveness of classifiers.
After model building, knowing the power of model prediction on a new instance, is very important issue.
Once a predictive model is developed using the historical data, one would be curious as to how the model will
perform on the data that it has not seen during the model building process. One might even try multiple model types for the same prediction problem, and then, would like to know which model is the one to use for the real-
world decision making situation, simply by comparing them on their prediction performance (e.g., accuracy). To
measure the performance of a predictor, there are commonly used performance metrics, such as accuracy, recall
etc. First, the most commonly used performance metrics will be described, and then some famous estimation
methodologies are explained and compared to each other. "Performance Metrics for Predictive Modeling In
classification problems, the primary source of performance measurements is a coincidence matrix (classification
matrix or a contingency table)”. Above figure shows a coincidence matrix for a two-class classification problem.
The equations of the most commonly used metrics that can be calculated from the coincidence matrix are also
given in Fig 12.
Figure 1: Confusion Matrix
Figure 4: Confusion Formula
As being seen in above figure, the numbers along the diagonal from upper-left to lower-right represent
the correct decisions made, and the numbers outside this diagonal represent the errors. "The true positive rate
(also called hit rate or recall) of a classifier is estimated by dividing the correctly classified positives (the true
positive count) by the total positive count. The false positive rate (also called a false alarm rate) of the classifier is
estimated by dividing the incorrectly classified negatives (the false negative count) by the total negatives. The
overall accuracy of a classifier is estimated by dividing the total correctly classified positives and negatives by the
total number of samples.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 469
5. LITERATURE SURVEY
Face detection is a computer technology that determines the location and size of human face in arbitrary
(digital) image. The facial features are detected and any other objects like trees, buildings and bodies etc., are
ignored from the digital image. It can be regarded as a ‗specific case of object-class detection, where the task is
finding the location and sizes of all objects in an image that belong to a given class. Face detection, can be
regarded as a more ‗general case of face localization. In face localization, the task is to find the locations and
sizes of a known number of faces (usually one). Basically there are two types of approaches to detect facial part
in the given image i.e. feature base and image base approach. Feature base approach tries to extract features of the
image and match it against the knowledge of the face features. While image base approach tries to get best match
between training and testing images.
Figure 5: Detection Method
6. FEATURE BASE APPROCH:
Acive shape models focus on complex non-rigid features like actual physical and higher level appearance
of features Means that Active Shape Models (ASMs) are aimed at automatically locating landmark points that
define the shape of any statistically modelled object in an image. When of facial features such as the eyes, lips,
nose, mouth and eyebrows. The training stage of an ASM involves the building of a statistical
a) Facial model from a training set containing images with manually annotated landmarks.
ASMs is classified into three groups i.e. snakes, PDM, Deformable templates
b) 1.1) Snakes: The first type uses a generic active contour called snakes, first introduced by Kass et al. in
1987 Snakes are used to identify head boundaries [8,9,10,11,12]. In order to achieve the task, a snake is first
initialized at the proximity around a head boundary. It then locks onto nearby edges and subsequently assume the
shape of the head. The evolution of a snake is achieved by minimizing an energy function, Esnake (analogy with
physical systems), denoted as Esnake = Einternal + EExternal Where Einternal and EExternal are internal and
external energy functions. Internal energy is the part that depends on the intrinsic properties of the snake and
defines its natural evolution. The typical natural evolution in snakes is shrinking or expanding. The external
energy counteracts the internal energy and enables the contours to deviate from the natural evolution and
eventually assume the shape of nearby features—the head boundary at a state of equilibria. Two main
consideration for forming snakes i.e. selection of energy terms and energy minimization. Elastic energy is used
commonly as internal energy. Internal energy is vary with the distance between control points on the snake,
through which we get contour an elastic-band characteristic that causes it to shrink or expand. On other side
external energy relay on image features. Energy minimization process is done by optimization techniques such as
the steepest gradient descent. Which needs highest computations. Huang and Chen and Lam and Yan both
employ fast iteration methods by greedy algorithms. Snakes have some demerits like contour often becomes
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 470
trapped onto false image features and another one is that snakes are not suitable in extracting non convex
features.
6.1 DEFORMABLE TEMPLATES:
Deformable templates were then introduced by Yuille et al. to take into account the a priori of facial
features and to better the performance of snakes. Locating a facial feature boundary is not an easy task because
the local evidence of facial edges is difficult to organize into a sensible global entity using generic contours. The
low brightness contrast around some of these features also makes the edge detection process. Yuille et al. took the
concept of snakes a step further by incorporating global information of the eye to improve the reliability of the
extraction process.
Deformable templates approaches are developed to solve this problem. Deformation is based on local valley,
edge, peak, and brightness .Other than face boundary, salient feature (eyes, nose, mouth and eyebrows)
extraction is a great challenge of face recognition.E = Ev + Ee + Ep + Ei + Einternal ; where Ev , Ee , Ep , Ei ,
Einternal are external energy due to valley, edges, peak and image brightness and internal energy
6.2 (POINT DISTRIBUTION MODEL):
Independently of computerized image analysis, and before ASMs were developed, researchers developed
statistical models of shape. The idea is that once you represent shapes as vectors, you can apply standard
statistical methods to them just like any other multivariate object. These models learn allowable constellations of
shape points from training example sand use principal components to build what is called a Point Distribution
Model. These have been used in diverse ways, for example for categorizing Iron Age broaches. Ideal Point
Distribution Models can only deform in ways that are characteristic of the object. Cootes and his colleagues were
seeking models which do exactly that so if a beard, say, covers the chin, the shape model can \override the image"
to approximate the position of the chin under the beard. It was therefore natural (but perhaps only in retrospect) to
adopt Point Distribution Models. This synthesis of ideas from image processing and statistical shape modelling
led to the Active Shape Model. The first parametric statistical shape model for image analysis based on principal
components of inter-landmark distances was presented by Cootes and Taylor in. On this approach, Cootes,
Taylor, and their colleagues, then released a series of papers that cumulated in what we call the classical Active
Shape Model.
6.3 LOW LEVEL ANALYSIS:
Based on low level visual features like color, intensity, edges, motion etc., Skin Color Base Color is
avital feature of human faces. Using skin-color as a feature for tracking a face has several advantages. Color
processing is much faster than processing other facial features. Under certain lighting conditions, color is
orientation invariant. This property makes motion estimation much easier because only a translation model is
needed for motion estimation. Tracking human faces using color as a feature has several problems like the color
representation of a face obtained by a camera is influenced by many factors (ambient light, object movement,
etc.,
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 471
.)
Figure 2: face detection
Majorly three different face detection algorithms are available based on RGB, YCbCr, and HIS color
space models. In the implementation of the algorithms there are three main steps viz.
Classify the skin region in the color space,
Apply threshold to mask the skin region and
Draw bounding box to extract the face image.
Crowley and Coutaz suggested simplest skin color algorithms for detecting skin pixels.
The perceived human color varies as a function of the relative direction to the illumination.
The pixels for skin region can be detected using a normalized color histogram, and can be normalized for
changes in intensity on dividing by luminance. Converted an [R, G, B] vector is converted into an [r, g] vector of
normalized color which provides a fast means of skin detection. This algorithm fails when there are some more
skin region like legs, arms, etc., Cahi and Ngan [6] suggested skin color classification algorithm with YCbCr
color space. Research found that pixels belonging to skin region having similar Cb and Cr values. So that the
thresholds be chosen as [Cr1, Cr2] and [Cb1, Cb2], a pixel is classified to have skin tone if the values [Cr, Cb]
fall within the thresholds. The skin color distribution gives the face portion in the color image. This algorithm is
also having the constraint that the image should be having only face as the skin region. Kjeldson and Kender
defined a color predicate in HSV color space to separate skin regions from background. Skin color classification
inHSI color space is the same as YCbCr color space but here the responsible values are hue (H) and saturation
(S). Similar to above the threshold be chosen as [H1, S1] and [H2, S2], and a pixel is classified to have skin tone
if the values [H,S] fall within the threshold and this distribution gives the localized face image. Similar to above
two algorithm this algorithm is also having the same constraint.
6.4 MOTION BASE:
When use of video sequence is available, motion information can be used to locate moving objects.
Moving silhouettes like face and body parts can be extracted by simply thresholding accumulated frame
differences. Besides face regions, facial feature scan be located by frame differences.
6.5 GRAY SCALE BASE:
Gray information within a face can also be treat as important features. Facial features such as eyebrows,
pupils, and lips appear generally darker than their surrounding facial regions. Various recent feature extraction
algorithms search for local gray minima within segmented facial regions. In these algorithms, the input images
are first enhanced by contrast-stretching and gray-scale morphological routines to improve the quality of local
dark patches and thereby make detection easier. The extraction of dark patches is achieved by low-level gray-
scale thresholding. Based method and consist three levels. Yang and huang presented new approach i.e. faces
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 472
gray scale behavior in pyramid (mosaic) images. This system utilizes hierarchical Face location consist three
levels. Higher two level based on mosaic images at different resolution. In the lower level, edge detection method
is proposed. Moreover this algorithms gives fine response in complex background where size of the face is
unknown
6.6 EDGE BASE
Face detection based on edges was introduced by Sakai et al. This work was based on analyzing line
drawings of the faces from photographs, aiming to locate facial features. Than later Craw et al. proposed a
hierarchical framework based on Sakai et al.‘s work to trace a human head outline. Then after remarkable works
were carried out by many researchers in this specific area. Method suggested by Anila and Devarajan was very simple and fast. They proposed frame work which consist three stepsi.e. Initially the images are enhanced by
applying median filter for noise removal and histogram equalization for contrast adjustment. In the second step
the edge image is constructed from the enhanced image by applying sobel operator. Then a novel edge tracking
algorithm is applied to extract the sub windows from the enhanced image based on edges. Further they used Back
propagation Neural Network (BPN) algorithm to classify the sub-window as either face or non-face.
6.7 FEATURE SEARCHING
VIOLA JONES METHOD:
Paul Viola and Michael Jones presented an approach for object detection which minimizes computation
time while achieving high detection accuracy. Paul Viola and Michael Jones [5] proposed a fast and robust
method for face detection which is 15 times quicker than any technique at the time of release with 95% accuracy
at around 17 fps. The technique relies on the use of simple Haar-like features that are evaluated quickly through
the use of a new image representation. Based on the concept of an ―Integral Image‖ it generates a large set of
features and uses the boosting algorithm AdaBoost to reduce the over complete set and the introduction of a
degenerative tree of the boosted classifiers provides for robust and fast interferences. The detector is applied in a
scanning fashion and used on gray-scale images, the scanned window that is applied can also be scaled, as well as
the features evaluated.
6.8 GABOR FEATURE METHOD
Sharif et al proposed an Elastic Bunch Graph Map (EBGM) algorithm that successfully implements face
detection using Gabor filters. The proposed system applies 40 different Gabor filters on an image. As a result of
which 40 images with different angles and orientation are received. Next, maximum intensity points in each
filtered image are calculated and mark them as fiducial points. The system reduces these points in accordance to
distance between them. The next step is calculating the distances between the reduced points using distance
formula. At last, the distances are compared with database. If match occurs, it means that the faces in the image
are detected.
6.9 CONSTELLATION METHOD All methods discussed so far are able to track faces but still some issue like locating faces of various
poses in complex background is truly difficult. To reduce this difficulty investigator form a group of facial
features in face-like constellations using more robust modelling approaches such as statistical analysis. Various
types of face constellations have been proposed by Burl et al. They establish use of statistical shape theory on the
features detected from a multiscale Gaussian derivative filter. Huang et al. also apply a Gaussian filter for pre-
processing in a framework based on image feature analysis. Image Base Approach.
6.10 LINEAR SUB SPACE METHOD
EIGEN FACES METHOD:
An early example of employing Eigen vectors in face recognition was done by Kohonen in which a
simple neural network is demonstrated to perform face recognition for aligned and normalized face images. Kirby
and Sirovich suggested that images of faces can be linearly encoded using a modest number of basis images. The
idea is arguably proposed first by Pearson in 1901 and then by HOTELLING in 1933 .Given a collection of n by
m pixel training. Images represented as a vector of size m X n, basis vectors spanning an optimal subspace are
determined such that the mean square error between the projection of the training images onto this subspace and
the original images is minimized. They call the set of optimal basis vectors Eigen pictures since these are simply
the Eigen vectors of the covariance matrix computed from the vectored face images in the training set.
© 2020 IJRAR September 2020, Volume 7, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)
IJRAR19L2063 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 473
Experiments with a set of 100 images show that a face image of 91 X 50 pixels can be effectively encoded using
only50 Eigen pictures.
6.11 STATISTICAL APPROCH
SUPPORT VECTOR MACHINE (SVM):
SVMs were first introduced Osuna et al. for face detection. SVMs work as a new paradigm to train
polynomial function, neural networks, or radial basis function (RBF) classifiers. SVMs works on induction
principle, called structural risk minimization, which targets to minimize an upper bound on the expected
generalization error. An SVM classifier is a linear classifier where the separating hyper plane is chosen to
minimize the expected classification error of the unseen test patterns. In Osunaet al. developed an efficient
method to train an SVM for large scale problems and applied it to face detection. Based on two test sets of
10,000,000 test patterns of 19 X 19 pixels, their system has slightly lower error rates and runs approximately30
times faster than the system by Sung and Poggio [16] . SVMs have also been used to detect faces and pedestrians
in the wavelet domain.
7. EXISTING SYSTEM
With the growth and popularity of social networking and messaging apps, online social networks (OSNs)
have become part and parcel of people’s lives. Most of the research done on social media network mining mostly
concentrates on discovering the knowledge behind the data for improving user’s life. Even though OSNs have
expanded peoples capability in increasing social contact but they have actually decreased face-to-face personal
interactions in real world due to this new terms like Phubbing and Nomophobia have been coined to define
people who are addicted to using social networking apps. Research has been already done in the past to explore
data mining techniques to detect various Social Network Mental Disorders like:
1) CYBER-RELATIONSHIP (CR) ADDICTION, which includes the addiction to social networking,
checking and messaging to the point where social relationships to virtual and online friends become more
important than real-life ones with friends and families;
2) NET COMPULSION (NC), which includes compulsive online social gaming or gambling, often
resulting in financial and job-related problems; and
3) INFORMATION OVERLOAD (IO), which includes addictive surfing of user status and news feeds,
leading to lower work productivity and fewer social interactions with families and friends offline.
In the existing research feature analysis is performed on large data sets by feeding the data of users to
machine learning framework namely Social Network Mental Disorders Detection to identify the various mental
disorders. Multimedia data like images where not considered for this analysis.
In this work we will be analyzing online images of users and perform emotions analysis using image
processing through Facial Expression Recognition (FER) and using Gabor filters and Convolutional Neural
Networks (CNN) to predict whether person is suffering from social disorder.
In conventional FER system, the developed algorithms work on the constrained database. In the
unconstrained environment, the efficiency of existing algorithms is limited due to certain issues during image
acquisition. This study presents a detailed study on FER techniques, classifiers and datasets used for analyzing
the efficacy of the recognition techniques.
8. PROPOSED SYSTEM
The most important form of non-verbal communications is facial emotions of a person. We proposed a
deep learning approach to detect a person’s face emotions in OSN images by studying their facial features like
surprise, fear, disgust, anger, happiness, and sadness using the Gabor filters and Convolutional neural network by
studying their facial features. The system extracts features from the OSN images and then classifies these features
and accordingly determines mental state of Social Network User (SNU). We will be developing social media like
application (in python) wherein we can upload images (emotion based dataset) which will be fed to image
processing system for training purpose for feature extraction and classification. The system will be trained with
features of depression. Then the emotion features of these faces will be extracted for prediction of depression.
Based on the level of depression features the user will be predicted for particular state of emotion like anger fear,
disgust, happy etc.
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9. CONCLUSION
In this project we will try to automatically identify various Social Network Mental Disorders by
analyzing the OSN images using techniques of deep learning and Facial Expression Recognition to extract the
emotions of images and classify them as features. The proposed framework uses Gabor filter and Convolutional
Neural Network and is expected to extract sub features and determine the various types of emotions of online
users. Based on the facial expressions system will be able to determine whether the online user is in good or bad
mood. Also multiple screenshots recorded from the facial expressions will determine whether person is suffering
from Social Media Mental Disorder. As for the next step, we plan to increase dataset from multimedia, used for
training the Neural Network and extract features using technique like computer vision.
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