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Face Recognition SystemFace Recognition System

Team members:Team members:

RaniaRania samreensamreen(04088097)(04088097)

SahilaSahila khan (04088130)khan (04088130)

Guided By :Guided By :

Prof. Dr. Eshwar Tenneti

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Overview Neural Networks Existing System

Proposed System Introduction to Project Content based method Color based method Design goals

Architectural design Back algorithm applications Requirements References

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oWhat is Face Recognition?Identify some unique feature in the face

image of a persono How are we going to Recognize?

The system will verify the identity of anapplicant by using neural networks

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A neural network is a weight directed graph

that models information processing in the

human brain. A neural network usually involves a large

number of processors operating in parallel,

each with its own small sphere of knowledgeand access to data in its local memory.

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Problems with existing

system:

o Short Term Memory

o Tough Processo Time Consuming

o Less Secure

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Advantages obtained

through face

recognition system

o Do Not Have toMemorize

o Easy Process

o Fastest Process

o Secure

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The identification of individuals using facerecognition represents a challenging taskwith many applications in everyday life as

well as in high security applications. There are two basic face detection

techniques:

Content-based methods and color-basedmethods.

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oContent based face recognition system extracts the features of the face like

nose, mouth, lips and calculate the distances between them.

oStatistical model of mutual distance between facial features are used to

locate face in the image.

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By using color basedmethod we can takeinto considerationcolor based photos.

content based methodtakes up only grayscale image.

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The design affects the performance, reliability

and maintainability.

1. Performance 2. Reliable

3. Maintainability

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The entire system can be mainly subdivided into

three parts.

i) Extraction of eye from the face image(EEF)

ii) Creation of image matrix from the eye(CIM)

iii) Neural network

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Extraction of eye fromimage

Creation of matrix

From eye

Neural networks

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The input to the system is the face image of

the applicant.

Scan the image and store it in a directory.

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using a class we willgrab pixels from the

extracted portion ofthe image . we are calculating RGBvalues for each pixel.

the matrix will be abinary value of either 0or 1.

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The input is an image matrix number ofvalues of image

number of input neurons.

The input layer neuronsreceive a finite number ofinputs and then will computesthe weighted sum using the

corresponding weights.

The output is 0 or 1

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First, assumes random weights to the input layer and thenupdate the weight by calculating the error occurred. The

error is the difference between the desired output and the

actual output. The Learning rule used is error correctionlearning. Calculate delta in each stepnand network tends tocorrect that error. The weights corresponding to each input isstored in a file. The training data consists of a number of

input datas. The weights must be satisfiable for all thetrained input datas. The input values multiplied withweights, the resultant will be a binary string that will pointsto a particular string.

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Compute how fastthe error changes as

activity of an outputunit is changed

the total inputreceived by an output

unit is changed

weight on theconnection into an

output unit is

changed

activity of a unit inthe previous layer is

changed

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For a neuron with activation function the

delta rule for 's th weight is given by

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By following the above

steps the face can berecognized based on the

eyes of the person.

Face recognition can also be done using otherfeatures like lips or nose but we are opting for

eyes as it gives more accuracy in output.

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Face recognition system is mainly used for

security reasons:

Passports Airports

time attendance system

biometric access control system

visitor management system

Scanning for criminals

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Front End: C# .NET

Hardware Requirements:

Hard Disk : 40 GB

Ram : 256 MB

Main Processor : Pentium IV

Display Type : High Color 800 by600

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H. A. Rowley, S. Baluja, and T. Kanade, "Neural Network Based FaceDetection", IEEE Transactions on Pattern Analysis and MachineIntelligence, 20,January, pp. 23-38, 1998.

Eye Identification for Face Recognition with Neural Networkshttp://www.ia.hiof.no/prosjekter/hoit/html/nr2_96/eye_id.html

http://www.neurosolutions.com/products/ns/whatisNN.html http://www.neurotechnology.com/ http://http://ezinearticles.com/?Biometric-Face-Recognition-System---

Face-Recognition-Now-and-Then&id=5000156

http://www.face-rec.org

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