Uploaded by Subhashree Rautray

Emotion Reconition

This application is based on computer vision it classify the human facial
expression using deep neural network . There are certain advance
algorithms which makes possible to detect emotions from images and
real time videos .
Here in this project we have our generated model using haar cascade
classifier and convolutional neural network to classify the emotions as
“Angry , Happy,Neutral,Sad,Surprise ” .
Haar Cascade classifier is from the open source computer vision it
detects the faces from a given image or frames . After getting the output
from the classifier then using the neural network model to analyse and
detect what kind of emotions is having in the given input .
For neural network model we have trained the datasets using
convolutional neural network as it takes image directly as input ,gives
high degree of accuracy when use in several layers .It has important
properties like weight sharing , sparse vector, feature selection etc.
Facial Expression are complex and it is necessary to understand the
person intentions , emotions . As this gestures are extremely important
to have particular response.
The application aims to improve the quality of human –machine
interaction , to make system as more intelligent that impact human lives
to be in more positive way.
This application can also helps in several sector like in health care ,
gaming , finance industry.
As the communication is non-verbal it quiet make s difficult for the
machine to classify .But due to advancement in deep neural network
some how we have reached certain accuracy that helps in identifying the
human face expressions .
General Workflow
• This recognition system designed to determine the emotional
state of a person’s facial expressions .
• Initially, the system analyzes the image of the face, locates and
measures the characteristic .
• Then each part of the face is analyzed deeper and its features are
extracted. Features are presented as information vectors.
• The classification of vector features according to the
corresponding expression emotions is carried out by a multilayer
neural network, which is trained and used to classify facial
expressions according to the corresponding emotional category.
Scope of the project in the industrial usage :
Emotion recognition is already widely used by different companies and
can be used in society for a variety of reasons .
Several businesses can process images, and videos in real-time for
monitoring video feeds or automating video analytics, thus saving costs
and making life better for their users. In certain domains like
Healthcare Domain :
Emotion recognition can be helpful for people who are visual impaired ,
so they can have effectiveness in their communication by detecting
facial expressions . There is an application ready already from Affectiva
which is an emotion measurement technology from MIT’S media
lab.They have developed software to recognize human emotions on
facials cues or physiological responses .
This technology enables software applications to use a webcam to track
a user’s smirks,smiles,frowns and furrows , which measures the users
level of surprise , amusement or confusion .
The technology also allows a person's heart rate to be measured from a
webcam without the person wearing a sensor. This is accomplished by
tracking color changes in the person's face, which pulses each time the
Emotion Recognition in Automotive Industry
The Automative industry also enhancing their product with emotion
recognition technology . As car manufactures they emphasize more on
making cars more personal and safe for people to drive .
Using AI to help them understand the human emotions. Facial emotion
detection smart cars can alert the driver when he is feeling drowsy.
In gaming industry
Some video games create survey kind of while in testing phase of their
product .During this phase user asked to play game for certain amount
of time . Using facial recognition can be help to understand the which
kind of emotions an user is experiencing in real time .
Tracking emotion is can help the developers to make their game less
violent with more user friendly .
Thus it really have the industrial impact ,but can it be trusted It does
evaluate decisions at our facial expression .It automate certain
communication and understandings but question arises that really we
want the outcomes of our results being determined on this basis.