IRJET- IoT based Door Access Control using Face Recognition

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1222
IOT BASED DOOR ACCESS CONTROL USING FACE RECOGNITION
Harsha H Prasad1
1Student of Bangalore Institute of Technology
2Guided By Prof. Maya. B. S, Dept. of Computer Science and Engineering, V. V. Puram, Bangalore, Karnataka, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - In recent years, it is important to own a
reliable security system that can secure our assets as
well as to protect our privacy. The traditional security
system needs an individual to use a key, identification
(ID) card or password to access an area such as home
and workplace. However,thepresentsecuritysystemhas
many weaknesses wherever it is simply cast and taken.
Most doors are controlled by persons with the
employment of keys, security cards, countersign or
pattern to open the door. The aim of this paper is to
assist users for improvement of the door security of
sensitive locations by using face detection and
recognition. The proposed system mainly consists of
subsystems namely image capture, face detection and
recognition, email notification and automatic door
accessmanagement.Face RecognitionsupportedopenCV
is brought up because it uses Eigen facesandreducesthe
scale of face images without losing vital features, facial
images for many persons can be stored in the database.
The door lock can also be accessed remotely from any
part of the world through GSM Module installed by
sending message from the phone. The captured image
from pi camera will be sent to the authorized person
through email for safety purposes.
Key Words: OpenCV, Raspberry pi, GSM Module, Email,
Eigen faces
1. INTRODUCTION
Human beings are recognized bytheirdistinctivefacial
characteristics. In the face recognition approach, a
given face is compared with the faces stored in the
database in order to identify the person. The aim is to
search out a face in the database, which hasthehighest
similarity with the given face. In thefieldofbioscience,
face recognition technology is one among the fastest
growing fields. The need of face recognition in security
systems is attributed to the rise of commercial interest
and therefore the developmentoffeasibletechnologies
to support the development of face recognition. Major
areas of commercial interest comprises of bio science,
law enforcement and surveillance, human computer
interaction, multimedia management (for example,
automatic tagging of a particular individual within a
collectionofdigitalphotographs)smartcards,passport
check, Criminal investigations, access control
management. However, face detection is more
challenging because of some irregular characteristics,
for example, glasses and beard will results in detecting
effectiveness. Moreover, different sorts and angles of
lighting will make detecting face generate uneven
brightness on the face, which willhaveinfluenceonthe
detection and recognition process. To overcome these
issues, the system primarily used openCV based face
recognition system using Haar classifiers for face. The
main processing element is Raspberry pi. The pi
camera is employed to capture the imageandsenditto
the authorized person for security purposes. The
authorized person can remotely control the lock and
unlock mechanism of the door using GSM Module by
installing Subscriber Identification Module (SIM). The
entry log ofeveryunidentifiedpersoniscapturedusing
pi camera and picture is sent to the email address of
the prescribed user.
1.1 PROBLEM IDENTIFICATION AND PROJECT
OBJECTIVES
A) PROBLEM IDENTIFICATION:
a. POSE VARIATION:
Comparing pose is another fundamental challenge for
face recognition system. Some unavoidable problems
appear in the variety of practical applications such as
the people are not always front of the camera, so the
pose problem is a big problem for the face recognition
system to be detection. The difference between the
different persons under the same poses, it is difficult
for the computer to do the face identification when the
poses of the galleryimagesaredifferent.Posevariation
still presents a challenge for face recognition.
b. OCCLUSION:
The face recognition context, occlusion involves that
some parts of the face cannot be find. For example, a

face photography taken from a web camera could be
partially hidden behind column. The recognition
process couldn’t find of a full input face. Hence, the
absence of some parts of the face may lead to not find
full face. There are also objects that can occlude facial
features goggles, beards, certain haircuts, hats etc.
c. EXPRESSION:
Comparing faces with different facial expression is
another problem for some face recognition
applications. Faces undergo large deformations under
facial expressions. Human can easily handle this
variation, but the algorithms can have problems with
the expression databases. Face recognition under
extreme facial expression still remains an unsolved
problem, and temporal information can provide
significant additional information in face recognition
under expression. The performanceoffacerecognition
systemsignificantlydecreaseswhenthereisadramatic
expression on the face. Therefore, it’s important to
automatically find the best face of a subject from the
images. Using the neutral face during enrolment and
when authenticating, so that we can find the neutral
face of the subject from the six universal expression
like Happiness, unhappiness, anger, horror, surprise.
d. AGING:
Face recognition across aging is most challenging in
that it has to address all other variants as well. Pose
expression and illumination changes are bound to
happen for two imagesofapersontakenyearsapart.In
addition to this, textural properties of the skin can be
different as well makeup, eyeglasses, weightloss/gain,
hair loss, etc. The facial changes that occurduetoaging
are influenced by numerousenvironmentalfactorslike
solar radiation, smoking, drug usage, stress level, etc.
The different biological and environmental factors can
either delay or expedite the process of aging. Aging
results inchanges in both thehardandsoftfacialtissue
of an individual. Loss of tissue elasticity and facial
volume and alteration in skin texture are some of the
other changes with aging. Drifts in facial landmarks
appear to reasonably characterizetheshapevariations
associate with aging, especially in ages 2-18.
e. TRANSFORMATIONS:
The same face can be presented to the system at
different scales. This may happen due to the focal
distance between the face and the camera. As this
distance gets close the face image gets bigger. Head
orientations may change due to translations and
rotations.
f. ILLUMINATION:
Comparing two faces with different illumination isone
of the fundamental problems for face recognition
system. Face images of the same person can be taken
under different illumination conditions such as, the
position and the strength of the light source can be
modified like the ones.
B) PROJECT OBJECTIVE:
There are few objectives design face detection system.
The objectives of face detection are:
1. To design real time face detection system.
2. To utilize the face detection system based on Haar
classifier.
3. To develop face detection system using open CV.
4. Users could operate on a touch screen to select
entering the house by the house by recognizing face or
entering password. For face recognition, an image will
be captured by
2. BLOCK DIAGRAM
Fig -1: Block Diagram of of IOT based door lock or
unlock system using face recognition
The proposed system was built using a high
performance processor i.e., Raspberry Pi model which
runs on a Debian based Linux Operating system called
Raspbian. Initially, the PIR sensor senses the presence
of human at the door. As and when a human is
detected, the Pi camera captures the image of the
person andsendstheimagetotheremoteuserthrough
mail. Haar face recognition algorithm is run on the
captured image using OpenCV in the Raspbian on the
basis of the images saved in the system. If the face is
recognized it implies that an authorized person is
trying for the door access and hence, the door lock is

opened, if the face is not recognized, then the remote
user can check the mail for the image of the person
trying to access the door and allow or deny the access
of the door throughnormalmessagefromthemobile.If
the user sends “allow” from the mobile, the door is
opened and if the user sends “deny” from the mobile,
the person trying to access the door is denied from
accessing it.
Raspberry Pi Module: The Raspberry Pi 3 model B
is considered as a portable mini-computer. It is a quad
core 64-bit Cortex- A53 processor with a very high
performance. It features a Broadcom SoC that has an
ARM compatible CPU with a speed of 1.2 GHz and also
has an on-chip GPU. It has an on-board memory of 1GB
RAM. The processor generally runs on a Debian based
Linux OperatingsystemcalledRaspbianbutalsocanbe
run using other third party operating systems like
Windows10 IOT, Ubuntu, etc., An SD card is used to
store the OS and program memory. The Raspbian OS
supports programming flexibility as it provides a
variety of programming platforms like C, C++, Python,
Java, etc., The processor has a number of GPIO pins for
lowerlevelinput/output.Italsosupportsprotocolslike
I2C. The processor has slots for USB, HDMI, composite
video output, and a 3.5mm jack for audio output,
ethernet, etc,.
PIR Sensor Module: The PIR(PassiveInfrared)sensor
used is HCSR-501. the sensorisusuallyusedforhuman
and motion detections. it is a digital sensor that senses
the presences of human beings. human body emits
thermal radiations of the range 0.8- 0.14nm. the PIR
sensor capturesthesethermalradiationsandgives1or
0 for presence or absence of a human being. the range
of the sensor is 10-15cm.
Pi Camera Module: Pi camera is a camera module
specially designed forRaspberryPi.Itisan8MP,1080p
resolution camera. it is mainly used for HD video
recording and to capture still photographs. It is
attached to the Raspberry Pi viaa15cmribboncableto
the CSI port. While programming, Picamera python
libraries are added to access the camera.
Servo Motor: It runs with a 12V DC power supply. It is
a rotary or linear actuator for controlling angular,
linear position, velocity and acceleration. It makes use
of the closed loop control mechanism. They areusedin
various applications like robotics, machinery, etc.,
OpenCV(Open Source Computer Vision): OpenCV is
an open platform for programmers for real time
computer vision and computations. It supports many
libraries of programming functions. It is built on C++
and has bindings with Java, Python and MATLAB. It
runs on a huge variety of platforms like Windows,
Linux, Android, iOS, macOS,andmanymore.Imageand
Video processing are two of the main applications of
OpenCV.
GSM MODULE: GSM module is used to send a message
to the authorized people based on the output. If the
output is positive “Information matched Access
granted” message willbesenttotheauthorizedpeople,
otherwise in case of unauthorizedaccessitwillsendan
“Access denied. Some unknown person is trying to
unlock the door”. Message to the certified users of the
system. As with any system, the initial stage is to
visualize the core modules, inputs, outputs and
interrelations among them;thiscanbeeffectivelydone
using a flow chart, above is a graphical representation
of the proposed system it can be installed and used on
any devices. It is a fast, secure and an easy application.
2.1 Viola-Jones Face Detection Method
This method consists of three mainsteps.Thefirststep
of the Viola-Jones face detection algorithm is to turn
the input image into a new imagerepresentationcalled
an integral image that allows a very fast feature
evaluation. The used features are reminiscent of Haar
basis functions. The Viola-Jones method analyzes a
24*24 sub-window using features consisting of two or
more rectangles. Each feature results in a single value
which is calculated by subtracting the sum ofthewhite
rectangle(s) from the sum of the black rectangle(s).
The different types of features are shown in below
figure.
Fig -2: Different types of features
For a fast processing of these features, the integral
image representation is used. This is done by making
each pixel equal to the entire sum of all pixels above
and to the left of the concerned pixel. It is calculated by
the following equation

Where (x’ , y’) is the integral image and (x , y) is the
original image. The integral image can be computed in
one pass over the original image by usingthefollowing
pair of recurrences:
s x , y = s x , y − 1 + i(x , y)
x , y = ii x − 1 , y + s(x , y)
where s x , y is the cumulative row sum, s x , −1 =0, and
ii −1 , y =0. The second step is constructing a classifier
in order to select a small number of importantfeatures
using AdaBoost learning algorithm. AdaBoost is a
machine learning boosting algorithm capable of
constructing a strong classifier through a weighted
combination of weak classifiers. where x is a 24*24
pixel sub-window of animage,fistheappliedfeature,p
indicates the direction of the inequality, and θ is a
threshold that decides whether x should be classified
as a positive (a face) or a negative (a non-face).The
final strong classifier is obtained after applyingtheada
boost algorithmdetailed.Inthethirdstep,thecascaded
classifier is used to determine whether a given sub-
window classifier is definitely not a face or maybe a
face. The cascaded classifier is composed of stages in
which each consists of a strong classifier.
3. CONCLUSION
The project is good example of Raspberry pi and pi
camera with Open CV.A face recognition system using
Raspberry Pi was developed. The system was
programmed by Python programming language. Both
Real times face recognition from specific images, i.e.
Stored images. The efficiency of the system was
analysed in terms of face recognition rate.Theanalysis
revealed that the present system shows excellent
performance efficiency.
ACKNOWLEDGEMENT
Those who walk the difficult path to success never at
this destiny they walk ahead towardsgreatersuccess.I
consider myself lucky to work with guidance of such
talented and experienced people during. The
preparation of Project Report who guided me all
through it.
A special mention must go to my Guide Prof. Maya. B. S
who supported me with her knowledge, experience
and suggestion. Only their inspiration has made this
Project report easy and interesting.
I am indebted to HOD Dr. Asha. T for her support at
various stages during the information of this piece of
work.
Last but not list I am thankful to all Teachers and Staff
members of CS&E department for their expert
guidance and continuous encouragement through out
to see that maximum benefit is taken out of his
experience.
REFERENCES
[1] Sahani M. Nanda C. Sahu A. Pattnaik B. “Web
Based Online Embedded Door Access Control
and Home Security System Based on Face
Recognition”.
[2] Rajat Bhise, Nikilesh Phadnis, Rahul Bari, Vijay
Dhage “ Iot Based Door Lock And Unlock
System Using Face Recognition,”
International Research Journal of Engineering
and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 12 | Dec 2018.
[3] Hteik Htar Lwin, Aung Soe Khaing, Hla Myo
Tun“AutomaticDoorAccessSystemUsingFace
Recognition,”InternationalJournalOfScientific
& Technology Research Volume 4, Issue 06,
June 2015.
[4] Sowmiya, U. shafiq mansoor J. 2015 Raspberry
pi based home door security through 3g
dongle.

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