A Mental Health Monitor using facial expression technology is an innovative and empathetic tool designed to assess and support individuals' emotional well-being. By analyzing facial expressions in real-time, this advanced technology can detect various emotions such as happiness, sadness, anxiety, and stress.

1. Mental Health Monitor
NIKHIL PAL(RA2011033010178)
PRASOON GAUTAM (RA2011033010189)
using Facial Expression
Guide Name:Dr. S. Sadagopan
Department of Computational
Intelligence,

2. The project aims to develop a mental health monitoring system solely
utilizing facial expression analysis. This system will employ computer vision
techniques to analyze facial expressions and emotions as indicators of
potential mental health issues such as anxiety, depression, or stress. Deep
learning models will be utilized to extract relevant features from facial
images. To create this system, a dataset of facial images will be collected
from individuals with and without mental health issues, with annotations
indicating the presence or absence of these issues. The system's
performance will be assessed using various metrics to determine its
accuracy in identifying potential mental health concerns through facial
expressions. Successful implementation of this system could facilitate early
identification of individuals at risk of mental health issues, enabling timely
intervention and support from healthcare professionals and organizations.
Abstract

3. Introductio
n
Emotion recognition is being actively explored in Computer Vision research. With the recent rise and
popularization of Machine Learning and Deep Learning techniques, the potential to build intelligent systems
that accurately recognize emotions became a closer reality. However, this problem is shown to be more and
more complex with the progress of fields that are directly linked with emotion recognition, such as psychology
and neurology. Micro-expressions, electroencephalography (EEG) signals, gestures, tone of voice, facial
expressions, and surrounding context are some terms that have a powerful impact when identifying emotions
in a human . When all of these variables are pieced together with the limitations and problems of the current
Computer Vision algorithms, emotion recognition can get highly complex.
Facial expressions are the main focus of this systematic review. Generally, an FER system consists of the
following steps: image acquisition, pre-processing, feature extraction, classification, or regression

4. To ensure consistent data for analysis,
addressing rotation, scale, and noise is
essential. Rotation correction aligns facial
landmarks horizontally,standardizes ROI
sizes, and background removal filters out
irrelevant information.
Data augmentation (DA) expands the
training dataset, reducing the risk of
overfitting, while Principal
Component Analysis (PCA) aids in
dimensionality reduction, optimizing
feature representation
Face detection is the initial step in FER,
responsible for selecting the Region of
Interest (ROI).Most FER papers utilize the
Viola–Jones face detector due to its
effectiveness
Precise emotion recognition relies on
extracting crucial facial features, including
methods like LBP, OF, AAM etc. These
techniques enable accurate emotion analysis
by effectively capturing and representing
facial expressions.
Geometric Transformations Face Detection
Feature Extraction
Image Processing
Proposed System

5. Application
Automotive Safety and
Research Systems
For the safety of the driver and
passengers by recognizing the facial
expression of the driver
Medical Research into
Autism
Ability to read the facial expression of
people with autism and finding the
best solutions for
them.
Market Research
Facial expression marketing to help
get the necessary information
regarding respective
trends.
Security and Access
Control
Facial expression analysis can be
integrated into security systems for
access control.

6. Technical challenges
Emotion recognition shares a lot of challenges with detecting moving
objects in the video identifying an object, continuous detection,
incomplete or unpredictable actions, etc.
Data augmentation
As with any machine learning and deep learning algorithms, ER solutions
require a lot of training data. This data must include videos at various
frame rates, from various angles, with various backgrounds, with people
of different genders, nationalities, and races, etc.
Problems
Faced

7. Action Plan

8. Author Year Title Objective Methods Results
Anil et al. 2018
A Survey on Facial
Expression Recognition
Techniques
To survey the techniques used
for facial expression recognition,
along with the accuracies
measured on various databases.
A brief comparison was made between the 2D and
3D techniques. The standard classification was
done which consisted of algorithms falling into
the category of geometrical features, appearance-
based features, and hybrid features.
The results showed that the hybrid
features-based algorithms
achieved the best performance.
Shaul Hammed
et al.
2022
Mental Health
Monitoring System
Using Facial
Recognition, PEN Test
and IQ Test
To develop a mental health
monitoring system that uses
facial recognition, PEN test, and
IQ test to detect early signs of
mental health problems.
The system uses a combination of facial
expression recognition, text analysis, and
cognitive assessment to identify users who may
be at risk of mental health problems.
The system was able to identify
users with depression, anxiety, and
stress with an accuracy of 80%.
Zhang et al. 2023
A Deep Learning
Approach for Mental
Health Monitoring Using
Facial Expressions
To develop a deep learning
approach for mental health
monitoring using facial
expressions.
The approach uses a convolutional neural
network to extract features from facial
expressions. The features are then used to train a
classifier to identify different mental health
problems.
The approach was able to identify
users with depression, anxiety, and
stress with an accuracy of 90%.
Liu et al. 2023
A Mobile Mental Health
Monitoring System
Using Facial Expression
Recognition
To develop a mobile mental
health monitoring system that
uses facial expression
recognition to detect early signs
of mental health problems.
The system uses a smartphone camera to capture
facial expressions. The facial expressions are then
sent to a cloud server for analysis. The cloud
server uses a deep learning model to identify
different mental health problems.
The system was able to identify
users with depression, anxiety, and
stress with an accuracy of 85%.
Literature Survey

9. Reference
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