This document discusses machine learning and neural networks. It provides information on supervised and unsupervised learning algorithms and applications such as speech recognition, driving cars, and data mining. It also describes the basic components of artificial neural networks including the input, hidden, and output layers. Finally, it summarizes the steps in a face recognition tool using neural networks including image acquisition, cropping, processing, and identification.

2. • Machine Learning
• Neural Networks
• Face Recognition Tool
• Output

3. Machine learning, a branch of
artificial intelligence, is concerned
with the construction and study of
systems that can learn from data.
For example, a machine learning
system could be trained on email
messages to learn to distinguish
between spam and non-spam
messages. After learning, it can
then be used to classify new email
messages into spam and non-spam
folders.

4. Problem Statement
Mathematical Description of a cost
Function.
Mathematical Description of how to
minimize the cost function.
Implementation

5. SUPERVISED LEARNING - algorithms are trained on
labelled examples, i.e., input where the desired output is
known. The supervised learning algorithm attempts to
generalise a function or mapping from inputs to outputs
which can then be used to speculatively generate an output
for previously unseen inputs.
UNSUPERVISED LEARNING - algorithms operate on
unlabelled examples, i.e., input where the desired output is
unknown. Here the objective is to discover structure in the
data (e.g. through a cluster analysis), not to generalise a
mapping from inputs to outputs.

6. – speech recognition
– drive automobiles
– play world-class backgammon
– program generation
– routing in communication networks
– understanding handwritten text
– data mining

7. Artificial neural networks are computational models
inspired by human central nervous systems (in particular
the brain) that are capable of machine learning and
pattern recognition. They are usually presented as systems
of interconnected "neurons" that can compute values from
inputs by feeding information through the network.
Like other machine learning methods, neural networks
have been used to solve a wide variety of tasks that are
hard to solve using ordinary rule-based programming,
including computer vision and speech recognition.

8. A neural network has 3 major components :
o An input layer
o Hidden Layers
o An Output Layer
Input is fed to the input layer if form of a vector. The
size of input layer depends on the size and features of
the input file used .
Hidden Layers process the input file to calculate the
cost of the hypothesis function(predicted output) using
various parameters(called neural network parameters).
The output layer gives the calculated cost of the input
data.

9. Image Acquisition – The image is taken as input either through the
webcam of the computer or through the hard disk of the system.
Cropping - The face to be identified is cropped from the rest of the
image.
Image Processing – All the images are changed into grayscale from
colour and are converted to a fixed scale and size.
Identification - This matrix is send as an input to a function which
uses the pre-calculated neural network parameters to calculate the
cost. This costs tells us to whom the image matches the most from the
training database.

12. THE OPENING INTERFACE WINDOW

13. WEBCAM CONNECTIVITY

14. SUCCESSFUL AUTHENTICATION

15. Thank You