1. 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 Parkhe2
, Kunal T. Kale3
, Komal H. Sarode4
, Pooja N. Bhoyar5
,Anil Khushwah6
1
Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, anuraagjoshi6@gmail.com
2
Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, divyanshu.parkhe@gmail.com
3
Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, kunaltkale11@gmail.com
4
Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, sarode193komal@gmail.com
5
Student, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, pooja.bhoyar05@gmail.com
6
Assistant Professor, Computer Engineering, Nuva College of Engg & Tech, Maharashtra, India, anilkushinfo@gmail.com
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.
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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.

2. APRL-16 ISSN: 2321-8134
http: // www.ijfeat.org (C) International Journal For Engineering Applications and Technology [137-140]
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.

3. APRL-16 ISSN: 2321-8134
http: // www.ijfeat.org (C) International Journal For Engineering Applications and Technology [137-140]
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

4. APRL-16 ISSN: 2321-8134
http: // www.ijfeat.org (C) International Journal For Engineering Applications and Technology [137-140]
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.
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