Classification of human faces and non faces using machine learning techniques

1. Classification of
Human Faces
and Non Faces
Using Machine
Learning
Techniques

2. Index
• Introduction
• Proposed Methodologies
• Result and Discussion
• Conclusion and Future Work

3. INTRODUCTION

4. • Everyday actions are
increasingly being handled
electronically, instead of
pencil and paper or face to
face.
• It is useful in day by day life.
For instance, There are
several different places which
are used cameras for different
purposes.

5. Business Companies

6. Airports

7. Marketing Agencies

8. Street Cameras

9. Play Grounds

10. PROPOSED
METHODOLOGY

11. • Step 1: Collect the database from
different persons for face expression
using MATLAB and collect the raw
data into excel sheet.
• Step 2: Pre-process and filter the
collected raw data into formatted format
and also apply filtration techniques like
replace missing values etc.
• Step 3: Classification using naïve
bayes, KNN, MLP, Decision tree using
Weka tool.

12. Naïve Bayes Algorithm
• It is based on Bayes' Theorem.
Naïve Bayes classifier can predict
class membership probabilities
such as the probability that a
given tuple belongs to a particular
class.

13. Naïve Bayes Algorithm Example

14. Decision Tree Algorithm
• A decision tree is a structure
that includes a root node,
branches, and leaf nodes.
The topmost node in the tree
is the root node.

15. Decision Tree Algorithm Example

16. Multi-Layer Perceptron (MLP)
Algorithm
• A multilayer perceptron (MLP) is
a class of artificial neural network.
An MLP consists of at least three
layers of nodes.
• Input Layer: This layer generates the input for
the network.
• Hidden Layer: The layer that maps the input to
the corresponding output is named as a hidden
layer.
• Output Layer: The layer from where the
resultant can be seen.

17. Multi-Layer Perceptron (MLP)
Algorithm Example

18. K-Nearest Neighbor (k-NN) Algorithm
• k-NN is a type of instance-based
learning, or lazy learning, is
a non-parametric method used
for classification and regression.
Both for classification and
regression, a useful technique can
be to assign weight to the
contributions of the neighbors, so
that the nearer neighbors
contribute more to the average
than the more distant ones.

19. K-Nearest Neighbor (k-NN) Algorithm
Example

20. RESULT
AND
DISCUSSION

21. Dataset Creation in MATLAB

22. Dataset uploading

23. Data Set Filtering

24. CLASSIFICATION
RESULTS

25. a) Decision Tree Classification Algorithm

26. b) Naïve Bayes Classification Algorithm

27. c) Multi-Layer Perceptron Classification
Algorithm

28. d) KNN Classification Algorithm

29. Representing the accuracy of the
different classification techniques
95.65
71.73
90.57
97.1
0
20
40
60
80
100
120
Decision Tree Naïve Bayes MLP KNN
Accuracyin%age
Techniques
Accuracy Comparison

30. Class Parameters comparison of
different classification Algorithms
0.95 0.957 0.952
0.729 0.717 0.717
0.908 0.906 0.895
0.966 0.971 0.967
0
0.2
0.4
0.6
0.8
1
1.2
Precision Recall Fmeasure
Values
Parameters
Class Parameters Comparison
Decision Tree Naïve Bayes MLP KNN

31. CONCLUSION
AND
FUTURE WORK

32. • In this project, facial emotions
classification is done using Machine
Learning Classifiers. Each facial
expression features are extracted and
categorized into 7 categories: Happy, Sad,
Angry, Disgust, Surprise, Neutral and
Fear.

33. Future Work…
• In future, the performance of
KNN algorithm can be improved
further and also the analysis can
be done on speech and another
dataset.

34. THANK YOU