About human recognition system algorithm based

1. PRESENTED BY
SOUMI SARKAR
B.TECH CSE
ROLL NO.-91/CSE/131036
HUMAN EMOTION
RECOGNITION SYSTEM

2. Human emotion detection is a very challenging field that target methods
to make effective human computer interaction. We know that emotions
play a major role in a Human life. At different kind of moments or time
human face reflects that how he/she feels or in which mood he/she is.
Here we use an existing simulator which will be able to capture human
emotions by reading or comparing facial expressions.
INTRODUCTION

3. • Emotion is
soften intertwined with mood, temperament, person
ality, disposition, and motivation. Human emotions
help us cope with everyday life, allowing us to
communicate what we feel toward certain
situations, people, things, thoughts, senses, dreams,
and memories.
• Many psychologists believe that there are six main
types of emotions, also called basic emotions. They
are happiness, anger, fear, sadness, disgust, and
surprise.
HUMAN EMOTIONS

4. • To analyze the limitations with existing system Emotion recognition
using brain activity.
• Preprocessing and resize the image.
• To detect the edge and reduce the size.
• To extract the feature of the face.
• Find the difference between the input image and the certified images
(stored in knowledge base).
• Recognition of emotions is based on the calculation of distances
between various features points.
OBJECTIVE

5. DESCRPTION OF THE TECHNIQUE
In this emotion recognition system there are various different techniques.
Typically, an automated face expression recognition system includes a
camera for capturing the facial image. It is then preprocessed so as to
minimize the environmental and other variations in the image. This
includes the operations of image scaling and brightness adjustment. After
that face ,mouth and eye region was detected i.e. feature extraction. Then
with the help of eyes and lips feature we classify five different emotions.

6. HOW EMOTION RECOGNITION SYSTEM
WORKS:
INPUT IMAGE
IMAGE PROCESSING
AND RESIZE
EDGE DETECTION
FACE DETECTION
EMOTION
RECOGNITION
DISTANCE MESUREMENT
EMOTION RECOGNITION
FEATURE EXTRACTION

7. Knowledge Base
It contains certified images which we will use for comparisons for the sake of
emotion recognition. These images are highly qualified and these are stored in
given database.
Pre-processing and resize
The image pre-processing procedure is a very important step in the facial
expression recognition task. The aim of the pre-processing phase is to
obtain images which have normalized intensity, uniform size and shape.

8. Color space transformation and lighting
compensation
In order to apply to the real-time system, we adopt skin-color detection
as the first step of face detection. We select this transform to detect
human skin. However, the luminance of every image is different. It
results that every image has different color distribution.
High frequency noisy removing
The main goal of this step is to enhance input image and also remove
various type of noises. Noise is removed by using noise removal
algorithm.

9. Edge Detection
Edges are detected by using commands of image processing tool box in
MATLAB.
Size Reduction
A technique now commonly used for dimensionality reduction in
computer vision particularly in face recognition is principal components
analysis (PCA).

10. showing results of pre-processing step showing results after noise removal showing results of edge detection

11. FACE FEATURE EXTRACTION
Eye Detection
One common method is to extract the shape of the eyes, nose,
mouth and chin, and then distinguish the faces by distance and
scale of those organs.
Feature 1 width of left eye
Feature 2 width of right eye
Feature 3 width of nose
Feature 4 width of mouth corners
Feature 5 width of face

12. FACE DETECTION
Face localization aims to determine the image position of a single
face; this is a simplified detection problem with the assumption that
an input image contains only one face.
DISTANCE MEASUREMENT
If the features have n-dimensions then the generalized Euclidean
distance formula is used to measure the distance.

13. Emotion Recognition
Detection of emotions is based on the calculation of distances
between various features points. In this step comparison between
distances of testing image and neutral image is done and also it
selects the best possible match of testing image from train folder.

14. ALGORITHM
Step 1: Input a image f(x,y).
Step 2: Apply Enhancement and Restoration process to detect face
accurately and get g(x,y).
Step 3: % compare image with emotion categories and each category
have some different sort of images like happy, very happy or a sad
showing that he is happy and store them a array emotion where I reflects
section of emotion type and j reflecting section which actually have
image related to i.

15. For(i=1;i=section;size++)
For(j=1;j=total images in i;j++)
if (g(x like emotion[i][j])
Result[i]= %age of results;
%Result[i] gives the relative measurements of g(x,y) like emotion[i][j];
Break;
Step 4: For(i=1;i=section size;i++)
Print section "have" Result[i];
Step 5: Exit

16. PERFORMANCE ANALYSIS
The experimental result shows that our algorithm can identify 30
emotions in our test image. Besides, the identification of emotions this
algorithm also shows the distance of test image from neutral image and
the best match of test image from trained images. There by our proposed
algorithm is suitable for use in real-time systems with high performance.
After implementing the algorithm for Facial Expression Recognition
simulator I have used the performance or output of these results to
compare with other method's results.
Input image Output
anger=00.00%
happy=90.00%
sad=04.50%
surprised=03.30%
neutral=02.20%

17. CONCLUSION
In this technique we have analyzed the limitations of existing system
Emotion recognition using brain activity. In 'Emotion recognition using
brain activity' brain activities using EEG signals has been used which is
toughest task to do as it become expensive, complex and also time
consuming when we try to measure human brain with
Electroencephalography (eeg).
Even when they have used existing data their result of analysis were 31
to 81 percentages correct and from which even by using Fuzzy logic 72
to 81 percentages only for two classes of emotions were achieved. This
technique also gives us idea that we can sense human emotions also by
reading and comparing the faces with images or data which is stored in
knowledge base. In this technique by using a system which is trained by
neural networks we have achieved up to 97 percent accurate results.

18. FUTURE WORK
However ,we have seen that we have got good results by using a
simulator which uses neural network. But still this simulator has some
limitations like at a time it will give results in one answer like Yes/No so
for future work we will try to add Fuzzy logic membership function in it
as one input may belong to different areas than only to a single one.

19. REFERENCES
A Method for Face Recognition from Facial Expression
By Sarbani Ghosh and Samir K. Bandyopadhyay
I.J. Image, Graphics and Signal Processing, 2012, 8, 50-56
Published Online August 2012 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2012.08.07
Neha Gupta and Prof. Navneet Kaur / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 4, Jul-Aug 2013, pp.2002-2006
https://en.wikipedia.org/wiki/Emotion_Recognition

20. Thank You