Facial recognition

Facial RecognitionUsing Neural Networks

Contents• Machine Learning• Neural Networks• Face Recognition Tool• Output

Machine LearningMachine 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.

The machine learning algorithm development

pipelineProblem Statement

Mathematical Description of a cost Function.

Mathematical Description of how to minimize the cost function.

Implementation

Types of Machine Learning Algorithms

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.

• Machine learning algorithms have been used in:

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

Artificial Neural Networks

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.

A neural network has 3 major components :o An input layero Hidden Layerso 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.

Face Recognition Tool 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.

Algorithm

DFD of System

OutputTHE OPENING INTERFACE WINDOW

WEBCAM CONNECTIVITY

SUCCESSFUL AUTHENTICATION

Thank You