PROJECT – A REPORT

on

FACE RECOGNITION TO RETREIVE DATA FROM DATABASE.

Submitted in partial fulfillment of the

requirements for the degree of

BACHELOR OF ENGINEERINGIn

INFORMATION TECHNOLOGYBy

Group No. 34Roll No. Names127 Rohit Bachwani128 Kailash Nadkar

129

130

Chirag

Pardasani

Rushabh ShahUnder the guidance of

Mrs. Sampada Pinge

(Associate Professor, Department of Information

Technology, TSEC)

Thadomal Shahani Engineering College

1

University Of Mumbai

2014-2015

Project Report Approval for B. E.(I.T)

This project report entitled “FACE RECOGNITION TO RETREIVE

DATA FROM DATABASE” by Rohit bachwani, Kailash nadkar,

Chirag Pardasani and Rushabh shah is approved for the

degree of Bachelor of Engineering in Information

Technology of Mumbai University.

Examiners

1.---------------------------------------------

2.---------------------------------------------

2

Supervisor

1.---------------------------------------------

Chairman

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Date: 07.11.2014

Place: TSEC, Bandra.

THADOMAL SHAHANI ENGINEERING COLLEGE

UNIVERSITY OF MUMBAI

2014-2015

THADOMAL SHAHANI ENGINEERING COLLEGE

(Department of Information Technology)

CERTIFICATE

This is to certify that Rohit Bachwani, Kailash Nadkar,

Chirag Pardasani and Rushabh shah have satisfactorily

3

carried out the dissertation work entitled “FACE

RECOGNITION, RETRIEVAL OF DATA FROM DATABASE” for the

degree of Bachelor of Engineering in Information

Technology of Mumbai University.

_______________

Mrs. Sampada Pinge,

Information Technology Department,

Thadomal Shahani Engineering College,

Bandra (W), Mumbai-400050.

__________________

Dr. G.T. Thampi

PRINCIPAL

Thadomal Shahani Engineering College,

Bandra (W), Mumbai-400050.

Declaration

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We, Rohit Bachwani, Kailash nadkar, Chirag

pardasani and Rushabh shah, declare that this written

submission represents our ideas in our own words and

where others' ideas or words have been included, we

have adequately cited and referenced the original

sources. We also declare that we have adhered to all

principles of academic honesty and integrity and have

not misrepresented or fabricated or falsified any

idea/data/fact/source in our submission. We understand

that any violation of the above will be cause for

disciplinary action by the Institute and can also evoke

penal action from the sources which have thus not been

properly cited or from whom proper permission has not

been taken when needed.

-------------------------

----------------

Roll No. 127, Rohit

Bachwani

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----------------

Roll No. 128, Kailash

5

Nadkar

-------------------------

----------------

Roll No. 129,Chirag

Pardasani

-------------------------

----------------

Roll No. 130,Rushabh Shah

Date:8.11.2014

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TABLE OF CONTENTS

Sr. No Topic

Page No.

List of Figures…………………………………………………………….……….7

Abstract……………………………………………………………………..……..8

1.Introduction…………………………………………………………….…..9

1.1 Problem Definition…………………………………………………….10

1.2Relevance of Project…………………………………………………….....11

1.3 Scope of

Project……………………………………………….............11

2.Review of Literature.………………………………………………………12

2.1 Introduction to Face Detection……………………………………….…

13

7

3. Description...………………………………………..……………………....15

3.1 Proposed System…………………………………………..………….16

3.2 Methodology and Analysis……………………...…………...

…………..17

3.3 Design Approach……………………………….....……………………18

4. Implementation……………………………………………………………19

4.1 Software Requirements…………………….…………..

………....19

4.2 Functional Requirements…….……………….….

………...……..20

4.3 Non-Functional Requirements…………………………...

……….20

5.Further Work…………………………………………………………….....…21

8

6.References…………………………………………………....………..…….....22

Acknowledgment ……………………………………………………………..…..23

9

ABSTRACT

Face (facial) recognition is the identi cation of humans by the fi

unique characteristics of their Faces.Face recognition

technology is the least intrusive and fastest bio-metric

technology.It works with the most obvious individual identi er fi

the human face. With increasing se-curity needs and with

advancement in technology extracting information has become

muchsimpler. This project aims on building an application based

on face recognition using different algorithms and comparing the

results. The basic purpose being to identify the faceand

retrieving information stored in database. It involves two main

steps. First to identifythe distinguishing factors in image n

storing them and Second step to compare it with theexisting

images and returning the data related to that image. The various

algorithms usedfor face detection are PCA Algorithm and Gray

Scale Algorithm

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LIST OF FIGURES

1.Structure of face recognition

system……………………………………..16

11

CHAPTER 1

INTRODUCTION

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13

1. INTRODUCTION

The face is our primary focus of attention in social life

playing an important role in conveying identity and emotions. We

can recognize a number of faces learned throughout our lifespan

and identify faces at a glance even after years of separation.

This skill 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. Human face detection

by computer systems has become a major field of interest. Face

detection algorithms are used in a wide range of applications,

such as security control, video retrieving, biometric signal

processing, human computer interface, face recognitions and

image database management. However, it is difficult to develop a

complete robust face detector due to various light conditions,

face sizes, face orientations, background and skin colors.

1.1 PROBLEM DEFINITION

Considering an image, Face recognition is a particularly

complex task that involves detection and location of faces. The

human face is a very challenging pattern to detect and

recognize, because while its anatomy is rigid enough so that all

faces have the same structure, at the same time there is a lot

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of environmental and personal factors affecting facial

appearance. The main problem of face recognition is large

variability of the recorded images due to pose, illumination

conditions, facial expressions, use of cosmetics, different

hairstyles, presence of glasses, beard etc.

1.2 RELEVANCE OF THE PROJECT

Trying to nd a face within a large database of faces:fi In

this approach the system returns a possible list of faces from

the database. The most useful applications contain crowd

surveillance, video content indexing, personal identi cationfi

(example: drivers license), mug shots matching, etc.

Real time face recognition: Here, face recognition is used

to identify a person on the spot and grant access to a building

or a compound, thus avoiding security hassles. In this case the

face is compared against a multiple training samples of a person

1.3 SCOPE OF THE PROJECT

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Face detection has two primary tasks, first is to

identify(feature extraction) and second is to locate the

image(Matching it from database).The project basically focuses

on high level of technology consideration. The project contains

two steps feature extraction and decision making. Our motive is

to easily identify the image and quickly compare it from the

database and give the result.

.

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CHAPTER 2

REVIEW OF LITERATURE

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2. LITERATURE REVIEW

2.1INTRODUCTION

In recent years, face recognition has attracted much

attention and its research has rapidly expanded by not only

engineers but also neuroscientists, since it has many potential

applications in computer vision communication and automatic

access control system. Especially, face detection is an

important part of face recognition as the first step of

automatic face recognition. However, face detection is not

straightforward because it has lots of variations of image

appearance, such as pose variation (front, non-front),

occlusion, image orientation, illuminating condition and facial

expression.

2.2FACE RECOGNITION

Automatic recognition of people is a challenging problem

which has received much attention during recent years due to

its many applications in different fields. Face recognition is

one of those challenging problems and up to date, there is no

technique that provides a robust solution to all situations.

This paper presents a new technique for human face

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recognition.This technique uses an image-based approach towards

artificial intelligence by removing redundant data from face

images through image compression using the two-dimensional

discrete cosine transform (2D-DCT).The DCT extracts features

from face images based on skin color. A self-organizing map

(SOM) using an unsupervised learning technique is used to

classify DCT-based feature vectors into groups to identify if

the subject in the input image is “present” or “not present” in

the image database. Face recognition with SOM is carried out by

classifying intensity values of grayscale pixels into different

groups.

Given an image that consists of many objects our goal is to

detect humans faces, extract these faces and identify each face

using a database of known humans. So, our algorithm is divided

to three main steps, first: face detection, the proposed

algorithm depends heavily on the open-source library OpenCV in

this step. Second: facial feature extraction, an effective

method to extract facial features like eyes, nose and mouth

depending on their locations with respect to the face region is

developed. Third: similar face identification or image

searching; the goal of this step is to scan the database of

known faces to find the most similar faces to the faces

extracted from the test image in the first step.

Since face recognition is not a difficult task for human

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beings, selection of biologically motivated Gabor filters is

well suited to this problem. Gabor filters, modeling the

responses of simple cells in the primary visual cortex, are

simply plane waves restricted by a Gaussian envelope function

[1].An image can be represented by the Gabor wavelet transform

allowing the description of both the spatial frequency

structure and spatial relations. Convolving the image with

complex Gabor filters with 5 spatial frequency (v = 0,…,4) and

8 orientation (µ = 0,…,7) captures the whole frequency

spectrum, both amplitude and phase.

Euclidean distance based classifier is used which is obtained

by calculating of distance between image to test and available

images that are taken as training images. Using the given set

of values minimum distance can be found. In testing, for every

expression computation of

Euclidean distance (ED) is done between new image (testing)

Eigenvector and Eigen

subspaces, to find the input image expression classification

based on minimum Euclidean

distance is done. The formula for the Euclidean distance is

given by:

ED ¿∑√ (x2−x1)^2

The recognition rate for the system proposed is found to be

95%.

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CHAPTER 3

DESCRIPTION

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3. DESCRIPTION

3.1 PROPOSED SYSTEM

The previous sections illustrate di erent techniques and methodsff

of face detection and recog-nition. Each category of method

performs well in certain criteria and also has drawbacksas well.

Systems with robustness and certain level of accuracy are still

far away. Keepingin view case study the following architecture

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Image scanned

Database

Image scanned

Face detection

Face recognition

Recog/not-recognized

is proposed for the detection and recognitionsystem.As discussed

earlier that the robust system catering the needs of real world

situation is achallenging task. The images will be scanned by

scanner and stored into database. Again theimage will be scanned

and stored into the database. Now two images of the same

candidatewill be stored into the database. The rst step is tofi

select desired images from the databasethen for comparisons them

the next step is to detect faces from each image. The next

stepis to recognize that images as of the same candidate or not.

3.2 METHODOLOGY AND ANALYSIS

1.Image acquisition: - Images used for facial expression

recognition are static images and image sequences.

Ideally a face acquisition stage features on automatic

face detector that allows locating faces in complex

scenes with cluttered background.

2.Preprocessing: - Image preprocessing often takes the

form of signal conditioning together with segmentation,

location or tracking of the face or its parts.

3.Feature Extraction: - Feature Extraction methods can be

categorized according to whether they focus on motion or

deformation of faces and facial features, respectively.

Whether they act locally or holistically.

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4.Classification: - Expression categorization is performed

by classifiers. Covering parametric as well as non-

parametric techniques has been applied to the

automatic expression recognition problem.

5.Postprocessing: - it aims to improve recognition

Accuracy by exploiting

domain knowledge to correct classification errors.

3.3 DESIGN APPROACH

The system is designed with very simple approach. User need to

input images which has text to be recognized. The application will

process the image and will give the result which can be saved.

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We shall design an algorithm that does the following :

Acquire an image from within the device’s storage or

should let the user click an image using the device

camera.

The application must perform required pre- processing on

the acquired image.

Once the image is preprocessed , comes the stage of feature

extraction where each character in the processed image is

extracted and separated from the entire image.

Later the extracted characters should be matched with the

training set of characters stored in the database .

Finally , the image can be displayed to the user , also

allowing him to access the system.

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4. IMPLEMENTATION

4.1 RECOMMENDED REQUIREMENTS

Software:-

1) Processing: core 2 duo with processing speed 2.0 Ghz

2) Memory: 1GB RAM

50GB Hard Disk

4.2 FUNCTIONAL REQUIREMENTS

Image Acquisition: Importing image from Gallery or Capturing

image from camera.

Image Pre-Processing: Such Enhancing quality of image and

noise reduction.

Feature extraction: Focusing on characters of an

image.

Character Recognition & Display Result: Identifying the

extracted characters and and Exhibiting the result.

Storage of Converted File: Storing file in the

application itself.

Searching of Converted Image: Searching of Image.

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4.3 NON – FUNCTIONAL REQUIREMENTS

Reliability: Application should face minimum failure and it

should not crash.

Availability: The application should be available as and

when the end-user demands.

Simplicity: The application should be user friendly.

Accuracy: The application should provide high accuracy as

compared to existing system.

Performance: It should have low latency time between input

and the first glimpse of output as quick performance (speed)

is concerned.

Portability: All android smart phones from version 2.3

should be capable of hosting this application.

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5. FURTHER WORK

As we will initiate our application’s implementation the

further work that we endeavour to undertake is the designing of

the application then followed by its development.

During this implementation, we will try to incorporate some

commonly used file extensions while considering compatibility

with the application.

We also have envisioned our project to extend beyond the

explained herein. Any application should be updated from time to

time and thus we would work further to incorporate new

functionalities.

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6. REFERENCES

[1] Perkins D. , “ A definition of caricature and recognition”,

Studies in the Anthropology of Visual Communication, Vol. 2,

pp. 1-24, 1975.

[2]Rupinder Saini , “Facial expression recognition

techniques,database & classifiers” ,International journal ,Vol.2

Issue(June 2014).

[3]Avinash Kaushal , “Face detection using neural network &

gabor wavelet”,IJCST ,

Vol 1,Issue(Sept 2010).

[4]Aruna bhadu , ”Facial expression using DCT”,International

journal, Vol 2,Issue(July 2012).

[5]Williwam Robson Schwartz , ”Face identification using large

feature sets”, IEEE transactions, Vol 2(Issue April 2012).

[6] Walaa Mohamed,” Face recognition using variance estimation

and feature extraction”,International Journal, Issue(April

30

2013).

ACKNOWLEDGEMENT

We take this opportunity to express our sincere thanks and

gratitude to Mrs. Sampada Pinge, Department of Information

Technology,Thadomal Shahani Engineering college for her constant

assistance as well as for being our internal project guide, for

the consistent motivation & inspiration, support and critical

review of the project. She has taken the pains to go through the

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project and make the necessary corrections wherever required.

We thank our Head of the Department, Mr. Arun Kulkarni for his

excellent guidance and support. Also we would like to thank Dr.

G.T.Thampi, our principal, for providing the facilities and

infrastructure for the project and extending moral support.

We would also like to thank our institution- Thadomal Shahani

Engineering College and our faculty members for providing a

platform for improvement.

Lastly we would like to thank our colleagues for supporting and

encouraging us to move forward with such a simple but compelling

topic and providing valuable suggestions as feedback. We thank

our family for being there and always supporting us,

understanding our investment in the project and allowing us to

do whatever is needed.

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