This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.

1. Detection and recognition
of face using neural
network
Supervised By: Submitted By:
Dr. Nitin Malik Smriti Tikoo
14-ECP-015
Mtech 4th Sem(ECE)

2. Agenda
• Face detection
• Face detection algorithms
• Viola Jones algorithm
• Flowchart
• Faces and features detected
• Face Recognition and its need.
• Back Propagation
• Sigmoidal Function
• Flowchart
• Solution Methodology
• Binary Image
• Histogram
• Neural Network
• Results
• Conclusion
• References
• Publications

3. Face Detection
• Face detection is a computer technology being used in a
variety of applications that identifies human faces in digital
images.
• It refers to the psychological process by which humans
locate and attend to faces in a visual scene.
• Face-detection algorithms focus on the detection of frontal
human faces.
• It is analogous to image detection in which the image of a
person is matched bit by bit.
• Some facial algorithms identify by doing facial feature
extraction , or by analyzing relative position , size and or
shape of eyes , cheekbones etc
• These features are then used to search images with
matching features. Any facial feature changes in the
database will invalidate the matching process.

4. Face Detection Algorithm
• The Viola–Jones object detection framework is the
first object detection framework to provide
competitive object detection rates in real-time
proposed in 2001 by Paul Viola and Michael Jones.
• Although it can be trained to detect a variety of
object classes, it was motivated primarily by the
problem of face detection.
• This algorithm is implemented
in OpenCV as cvHaarDetectObjects().k

5. Viola Jones Algorithm
• The characteristics of Viola–Jones algorithm which make it
a good detection algorithm are:
• Robust – very high detection rate .
• Real time – For practical applications
• Face detection only (not recognition) - The goal is to
distinguish faces from non-faces (detection is the first step
in the recognition process).
• The algorithm has four stages:
• Haar Feature Selection
• Creating an Integral Image
• Ada boost Training
• Cascading Classifiers

6. Flow chart depicting the viola Jones
procedure
INPUT IMAGE
HAAR FETURE
SELECTION
INTEGRAL
IMAGE
ADABOOST
TRAINING
CASCADING
CLASSIFIERS

7. Face, nose and mouth detected

8. Eyes are detected

9. Face Recognition and its need
• Plays an integral part in human computer interaction.
• It is typically used in security systems and can be compared
to other biometrics such as fingerprint or eye iris
recognition systems.
• Recently, it has also become popular as a commercial
identification and marketing tool.
• Some facial recognition algo’s identify by doing facial
feature detection &extraction , or by analyzing relative
position , size and or shape of eyes , cheekbones etc
• The skill to identify a face is quite robust despite of large
variations in visual stimulus due to changing condition,
aging and distractions such as beard , glasses or changes in
hairstyle.
• Trivial for brain extremely difficult to imitate artificially.

10. Back Propagation
• Backward Propagation of Errors
• Most common & popular- for training NN.
• Requires known, desired o/p for each i/p in order to
calculate the loss function gradient.
• Considered supervised learning method.
• BP algo consists of 2 propagation:
• Forward Propagation : Input is fed through the network
to generate propagation's output activations.
• Backward propagation: Backward propagation of the
propagation's output activations through the neural
network using the training pattern target in order to
generate the deltas (the difference between the
targeted and actual output values) of all output and
hidden neurons.

11. Back Propagation Network
Architecture

12. Continued…..
• Gives insights into how changing the weights
and biases changes the overall network
behavior.
• Has a high rate of convergence .
• The design of back propagation architecture
lies entirely on the type of activation function
selected.
• The activation function used in this case is
sigmoidal activation function.

13. Sigmoidal Function
• A mathematical function having ‘S’ shape .
• Often it refers to the special case of logistic
function .
• S(t)=11+e(-t).
• Is a bounded differentiable real function
defined for all the real input values and has a
positive derivative at each point.

14. Sigmoid Function

15. Flow diagram of face recognition

16. Solution Methodology
• Image processing consisting of capturing the
facial image of a person.
• Face detection and feature detection.
• RGB to gray image conversion.
• Gray to Binary Conversion of images.
• Finding the histogram equivalent of the given
binary image.
• Neural Network Training and processing
procedure where it is trained using neural
network fitting tool .

17. Binary Image
• Is a digital image that has only two possible value
for each pixel.
• Typically the two colors used for a binary image
are black and white though any two colors can
used .
• The color used for the object in the image is the
foreground color while the rest of the image is
background color.
• In the scanning industry it is often referred as
bitonal.
• Uses im2bw keyword to convert an image to
binary.

18. Histogram
• A histogram is a graphical representation of
the distribution of numerical data. It is an
estimate of the probability distribution of a
continuous variable (quantitative variable) and
was first introduced by Karl Pearson.
• To construct a histogram, the first step is to "bin"
the range of values—that is, divide the entire
range of values into a series of intervals—and
then count how many values fall into each
interval.
• The bins are usually specified as consecutive,
non-overlapping intervals of a variable. The bins
(intervals) must be adjacent, and are usually
equal size.

19. Neural Network
• To work in neural network one needs to write a command nnstart
in the command window.
• nftool-Opens the neural network fitting tool GUI. It leads through
solving a data fitting problem ,solving it with a two layer feed –
forward network trained with Levenberg Marquardt Back
Propagation Algo.
• nntraintool opens the neural network training GUI.This function
can be called to make the training GUI visible before training has
occurred, after training if the window has been closed, or just to
bring the training GUI to the front.
• Network training functions handle all activity within the training
window.
• To access additional useful plots, related to the current or last
network trained, during or after training, click their respective
buttons in the training window.
• nntraintool close or nntraintool('close') closes the training
window.

20. Results

23. Neural network Training
procedure

25. Processing and training

26. Performance Plot of the Referred
Journal

27. Performance Plot 1

28. Performance Plot

29. Conclusion
• In this work it has been shown that if a facial image of a person is given
then the network can be able to recognize the face of the person. The
whole work is completed through the following steps :
• Facial detection is influenced by clarity of the image , colored or black
and white image .
• It can only support frontal detection of images.
• The training does takes a lot of time in order to separate a negative face
from a positive face.
• Using Adaptive boost algorithm and cascading helps in faster detection.
• Facial image without dividing into parts has been applied in the
network.
• Feed Forward Back Propagation neural network have been used to train,
test and validate the network for each part of the image using MATLAB.
• Training of each sample is performed up to 8 to 10 times to minimize the
mean square error.

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34. Publications
Journal Publications
• Smriti Tikoo, Nitin Malik, “Detection of face using Viola
Jones algorithm and recognition using Backpropagation
neural network”, International Journal of Computer science
and Mobile Computing, vol 5, issue no. 5, pg.288-295, May
2016.
• Communicated: “Detection, segmentation and recognition of
face and its features using neural networks”, International
Journal of Advanced Research in Computer and
Communication Engineering.

35. THANK YOU