april201629

APRL-16 ISSN: 2321-8134

http: // www.ijfeat.org (C) International Journal For Engineering Applications and Technology [137-140]

IJFEAT

INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND

TECHNOLOGY

TITLE: Face Recognition & Face Tracking using CCTV Surveillance via Android

Anurag B. Joshi1, Divyanshu Parkhe

2, Kunal T. Kale

3, Komal H. Sarode

4, Pooja N. Bhoyar

5,Anil Khushwah

6

1Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected] 2Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected]

3Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected] 4Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected] 5Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected] 6Assistant Professor, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, [email protected]

Abstract

In 21st

century, the crime has increased very rapidly and is still increasing. Not only in India but the whole world is facing hardships.

The Government of every country is implementing the new technology in order to control or monitor the criminal activities. Not only crime has increased but a new threat is faced by the world is Terrorism. CCTV Camera is one of the greatest piece of technology used

to track the criminal activity, but the main limitation of CCTV is that it is used to store and not to detect the crime in real time. This paper propose the Real Time Recognition and Reporting of Criminal by Face Recognition using Android Device. Seventy percent of

total population uses Android Devices. The main concept of this system is that it can track the targeted person with the help of CCTV Surveillance camera. The details of the desired or targeted person is provided to the server of CCTV cameras with the help of Android

Device and the details of the recognized person’s location is sent back to Android Device. It uses EIGEN VECTORS and EIGEN

MATRIX to identify or recognize the person's face with the existing database. Eigen vectors uses the vectors of face such as length of

lip edges, length of the nose from tip to forehead, length of eyes edges, length of forehead, etc.

Index Terms: CCTV, Android, Face Recognition, Eigen vectors, Eigen matrix, Database.

--------------------------------------------------------------------- *** ------------------------------------------------------------------------

1. INTRODUCTION

We identify any person through his face. When we meet

someone or see someone the first thing we see is there face.

Since face is such an important feature of humans, people use

it as an identity feature as it is also a biometric standard for

identification of a specific person just like finger prints. This

paper propose the face recognition framework using CCTV via

Android. It recognizes the desired person by extracting the

important facial features of the face. The facial features are

extracted either from a photo or live stream, and are stored in

the form of vectors. These vectors are used to match with the

face of the person in the frame. Now the main speciality of this

system is that it works in real time and reports about it in the

Android device. The person recognized in the camera sends its

IP address to the android device which is used to narrow down

the location to a minimest range because the CCTV camera

monitors a specific range of area which makes it easy to

narrow or pinpoint the location of that specific area where

CCTV camera is installed.

1.1 Purpose

The CCTV cameras store the footage of the specific area and

its footage is used to detect the criminals later, but this method

takes a long period of time. It also takes a long time of

investigation and is a manual task which requires man hours to

identify the culprit. This system automatically search the

records for the match and report about the culprit which makes

it easier for the authority to deliver justice in less time. It can

also find the missing persons if CCTV surveillance is available

in that area.

2. LITERATURE SURVEY The most powerful surveillance device available at present

which is also used as a piece of evidence commonly known as

Closed circuit TV which provides a live feed to user.

Development in technology has provided new abilities to this

technology such as grabbing minute details, changes in colour,

adaptability to light which has made it more reliable.

Networking: Nowadays CCTVs comes with IPs due to new

Internet protocols which enables them to transmit data without

any physical connection to any device which has access to

Internet and the IP of CCTV.

APRL-16 ISSN: 2321-8134


CCTVs are used at the places with high density populated

places like malls, airports, traffic terminals, banks. People also

use it for house and business to make them secure.

Due to the abundance of this system it is easy to implement it.

Table: System Requirements for Face Recognization

Minimum Maximum

OS Windows 7 Windows 10

Software .NET Framework 3.5 .NET Framework 4.5

Hardware CCTV camera, Androi d Device v 5.1, DB.

RAM(Comp.) 1Gb 4Gb

3. FACE RECOGNIZATION USING CCTV VIA

ANDROID

Fig: Working of Software

The Application sends data to the server which applies the

algorithm for extracting the facial features. The CCTV camera

identifies the person with the help of the software and if the

match is found it sends its IP address through which the

physical location of the camera is identified and the targeted

person is reached.

Fig-1: Extraction of vectors The figure

shows the facial features whose vectors are extracted and

stored for matching with the person present in the frame of

CCTV.

Fig-2: Vectors calculation

These vectors make the person recognizable even if they try to

make over like growing beard or trimming hair. Since the

vectors of face will remain same. But it does not work if the

targeted person covers his face or if he is an identical twin

since identical twins have the same facial vectors. In such

cases some more biometric tests must be done in order to

confirm his identity. This is where the system fails to deliver

the result.

APRL-16 ISSN: 2321-8134


Fig-3: Group frame reorganization

This system can recognize the faces in group and

identifies them from the database. There is no conflict

among the identity until they were twins or surgically

changed their appearance.

Fig-4: Eigen Face

Now due to the use of Eigen vectors and Eigen matrix the

system do not responds until its value is true. Even if the

expression of the person are changed it still recognizes

the person.

This model can also be implemented with 3D face reorganization techniques but for that a 3D model of the face must be made and the system must be trained for it.

4. Face Recognition Applications:

Face recognition is used for two primary tasks:

1. Verification (one-to-one matching): When

presented with a face image of an unknown individual

along with a claim of identity, ascertaining whether the

individual is who he/she claims to be.

2. Identification (one-to-many matching): Given

an image of an unknown individual, determining that

person’s identity by comparing (possibly after encoding)

that image with a database of (possibly encoded) images of

known individuals.

There are numerous application areas in which face recognition

can be exploited for these two purposes, a few of which are

outlined below.

1) Security: access control to buildings,

airports/seaports,

ATM machines and border checkpoints ;

computer/network security; email authentication on

multimedia Workstations.

2) Surveillance: a large number of CCTVs can be

monitored to look for known criminals, drug

offenders, etc.

3) General identity verification: electoral registration,

banking, electronic commerce, identifying newborns,

national IDs, passports, drivers’ licenses, employee

IDs.

4) Criminal justice systems: mug-shot/booking systems,

post-event analysis, forensics.

5) Image database investigations: searching image

databases of licensed drivers, benefit recipients,

missing children, immigrants and police bookings.

6) “Smart Card” applications: maintaining a database of

facial images, the face-print can be stored in a smart

card, bar code or magnetic stripe, authentication of

which is performed by matching the live image and

the stored template.

7) Multi-media environments with adaptive

humancomputer interfaces: part of ubiquitous or

context aware systems, behavior monitoring at

childcare or old people’s centres, recognizing a

customer and assessing his needs.

8) Video indexing: labelling faces in video.

9) Witness face reconstruction.

In addition to these applications, the underlying techniques in

the current face recognition technology have also been

modified and used for related applications.

5. CONCLUSION

This system is proposed for detecting and recognizing the

targeted or desired person from the live stream of CCTV

camera. It may be a criminal or terrorist or any missing person

until you have the details his face in format of photo or video

they can tracked down using CCTV cameras via Android

device. This software is designed so that it can reduce the man

hours and labour and make surveillance more powerful and

APRL-16 ISSN: 2321-8134


reliable. It can control and minimize the rate of criminal

activities.

ACKNOWLEDGEMENT

We are very thankful to Prof. Anil Khushwah sir for guiding

and supporting us throughout our work.

REFERENCES:-

[1]. Criminal Tracking System using CCTV. Prathamesh Mali, Vedant Rahane, Suraj Maskar, Avinash

Kumbhar ; SAOE, Kondhwa, Maharashtra,

India Volume 6, Issue 1, January 2016 ISSN: 2277 128X International Journal of Advanced Research in

Computer Science and Software Engineering

[2]. Faizan Ahmad, Aaima Najam and Zeeshan Ahmed

Image-based Face Detection and Recognition: “State

Of the Art”. IJCSI International Journal of Computer Science

Issues, Vol. 9, Issue 6, No 1, November 2012.

[3]. Real-time Face Detection and Classification for ICCTV

Brian C. Lovell Security and Surveillance Research Group.

[4]. Automated Facial Recognition in the Public and Private

Sectors Report prepared by the Research Group of the Office

of the Privacy Commissioner of Canada March 13 Privacy

Research Papers.

[5]. Seema Verma Prof. Sonu Agrawal A Study on “A Soft

Biometric Approach: Face Recognition”

International Journal of Advanced Research in Computer Science and Software Engineering. Volume 3, Issue 3, March 2013 ISSN: 2277 128X.

[6]. Shang-Hung Lin Ph.D. IC Media Corporation, An

Introduction to Face Recognition

Technology,Informing science special issue on multimedia informing technology.- part-2 voume 3 no.-1, 2000.

[7]. Face Detection and Recognition in an Image Sequence using Eigen edginess B S Venkatesh, S Palanivel and B Yegnanarayana Department of Computer Science and

Engineering. Indian Institute of Technology, Madras Chennai -

600 036

april201629

Documents

Transcript of april201629