scholarly journals Prototype of an Automatic Entrance Gate Security System Using a Facial Recognition Camera Based on The Haarcascade Method

2021 ◽  
Vol 2117 (1) ◽  
pp. 012015
Author(s):  
A Suryowinoto ◽  
T Herlambang ◽  
R Tsusanto ◽  
F A Susanto
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Criminal Activity ◽  
Relevant Information ◽  
Security System ◽  
Communication Process ◽  
Pass Rate ◽  
Raspberry Pi ◽  
Process Data ◽  
Front Door

Abstract This article aims to test a facial recognition-based front door security system, which can also convey relevant information to the owner’s mobile phone via an SMS gateway. This system is necessary to prevent unwanted criminal activity by the owner. The method used is Hercascade as face recognition for security. Use a set of webcam settings to compare human face objects in the background with face data already stored in the database. Capture images using a Raspberry PI connected to a USB webcam for the sensor, move the front door using a servomotor as a drive, and own the system in the form of a short message from basic communication process data Notify to. There is also an ultrasonic sensor as an activation system to detect human objects when they approach the door and invade. Based on the results of 90 tests on a system with varying distances of objects on the camera (30 cm, 40 cm, 50 cm), the average pass rate of the tests is 91.11%. We can conclude that face recognition by the Hercassette method can be applied as an entrance security system.

scholarly journals Face recognition for automatic vehicle ignition based on Raspberry Pi

2020 ◽  
Vol 1 (2) ◽  
pp. 53-68
Author(s):  
Alex V. Nuñez ◽  
Liliana N. Nuñez
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Data Gathering ◽  
Software Component ◽  
Security System ◽  
Raspberry Pi ◽  
Hardware Platform ◽  
And Training

In this project a facial recognition application for automatic vehicle ignition is developed. This application is built using a Raspberry Pi as the hardware platform and the OpenCV library for computer vision as the software component. In this research the different methods for automobile security are analyzed, as well as, the different methods used to perform face recognition.  The main goal of this application is to enhance the security system of the vehicle, allowing to ignite the vehicle only by register users. To achieve this goal three main processes are carried out, face detection, data gathering, and training the system to grant access through face recognition.

scholarly journals Smart Home Security System using Iot, Face Recognition and Raspberry Pi

2020 ◽  
Vol 176 (13) ◽  
pp. 45-47
Author(s):  
Manoj R. ◽  
Rekha Y. ◽  
Raju R. ◽  
Sharad A.
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security

scholarly journals ROBUST REPRESENTATION OF 3D FACES FOR RECOGNITION

2006 ◽  
Vol 20 (08) ◽  
pp. 1159-1186 ◽  
Author(s):  
A. BELÉN MORENO ◽  
ÁNGEL SÁNCHEZ ◽  
ENRIQUE FRÍAS-MARTÍNEZ
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Relevant Information ◽  
Large Set ◽  
Discrimination Power ◽  
3D Face ◽  
The Face ◽  
The Difference

Automatic face recognition is becoming increasingly important due to the security applications derived from it. Although the facial recognition problem has focused on 2D images, recently, due to the proliferation of 3D scanning hardware, 3D face recognition has become a feasible application. This 3D approach does not need any color information. In this way, it has the following main advantages in comparison to more traditional 2D approaches: (1) being robust under lighting variations and (2) providing more relevant information. In this paper we present a new 3D facial model based on the curvature properties of the surface. Our system is able to detect the subset of the characteristics of the face with higher discrimination power from a large set. The robustness of the model is tested by comparing recognition rates using both controlled and noncontrolled environments regarding facial expressions and facial rotations. The difference between the recognition rates of the two environments of only 5% proves that the model has a high degree of robustness against pose and facial expressions. We consider that this robustness is enough to implement facial recognition applications, which can achieve up to 91% correct recognition rate. A publish 3D face database containing face rotations and expressions has been created to achieve the recognition experiments.

scholarly journals Smart Security System Using IoT, Face Recognition and Processor to Processor Communication

2021 ◽  
Author(s):  
Indhuja G ◽  
Aashika V ◽  
Anusha K ◽  
Dhivya S ◽  
Meha Soman S
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Security System ◽  
Raspberry Pi ◽  
Security Systems ◽  
Serial Interface ◽  
Recognition Algorithms ◽  
Alert Message ◽  
Face Detection And Recognition ◽  
Present World

In the present world the security of the home, banks, shops, etc., are the prime concerns. The traditional security such as Closed-Circuit Television (CCTV) cameras are very easy to break and lead to theft. And moreover, the installation cost of the security systems is costlier. To overcome these problems, we are presenting Internet of Things (IoT) based solution where we can setup a smart security system. In this paper, we are proposing the system with the help of face detection and face recognition algorithms to secure our home which gives us the facility of entire surveillance of our buildings remotely and take appropriate action if anything goes wrong. The Camera Serial Interface (CSI) is attached to the Raspberry PI which detects presence of person using Face detection and recognition algorithms. The multiple Raspberry PIs attached in different areas of our buildings are connected to the main Raspberry PI which acts as hub module. If the person is identified as unknown, the information is sent to Hub module which in turn sends the alert message and live video streaming to the user using an app which we developed.

scholarly journals PENERAPAN EIGENFACE UNTUK COMPUTER BASED TEST (CBT) PENERIMAAN MAHASISWA BARU SEKOLAH TINGGI TEKNOLOGI ADISUTJIPTO

Compiler ◽  
2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Haruno Sajati ◽  
Astika Ayuningtyas ◽  
Dwi Kholistyanto
Keyword(s):  
Face Recognition ◽  
Computer Technology ◽  
Digital Image ◽  
Facial Recognition ◽  
Security System ◽  
The Internet ◽  
Accuracy Rate ◽  
The Face ◽  
Computer Based ◽  
Or Applications

One of the development of computer technology is the availability of systems or applications that help human work everyday so that can be resolved quickly and correctly. The system, one of which is Computer Based Test (CBT). CBT is an application used for tests conducted using computers that are in the application there are some features of CBT security when working on the problem. CBT can use a stand-alone computer, a computer connected to a network or a computer connected to the internet. Facial recognition is a type of biometric application that can identify specific individuals in a digital image by analyzing and developing face patterns. In its implementation, CBT has a weakness in the security system that becomes the gap of CBT users to commit fraud, therefore required a good security system with the creation of CBT applications that use eigenface algorithm. It is necessary to have a security system that overcomes the problem that is required identification of face recognition of participants during the test so that cheating can be reduced. The results of the test using eigenface algorithm accuracy rate reached 82%, some things that affect the level of accuracy is, the intensity of light, facial position and the use of accessories on the face.

scholarly journals IoT based facial recognition door access control home security system using raspberry pi

2020 ◽  
Vol 11 (1) ◽  
pp. 417
Author(s):  
Syafeeza Ahmad Radzi ◽  
M.K. Mohd Fitri Alif ◽  
Y. Nursyifaa Athirah ◽  
A. S. Jaafar ◽  
A. H. Norihan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Security System ◽  
Raspberry Pi ◽  
Small Computer ◽  
Home Security ◽  
Learning Technique ◽  
Access Control System ◽  
Computer Board ◽  
Internet Of Thing

The home security system has become vital for every house. Previously, most doors can be open by using traditional ways, such as keys, security cards, password or pattern. However, incidents such as a key loss has led to much worrying cases such as robbery and identity fraud. This has become a significant issue. To overcome this problem, face recognition using deep learning technique was introduced and Internet of Thing (IoT) also been used to perform efficient door access control system. Raspberry Pi is a programmable small computer board and used as the main controller for face recognition, youth system and locking system. The camera is used to capture images of the person in front of the door. IoT system enables the user to control the door access.

scholarly journals Smart Home Security Menggunakan Face Recognition Dengan Metode Eigenface Berbasis Raspberry Pi

2019 ◽  
Vol 8 (2) ◽  
pp. 48-56
Author(s):  
Rudi Kurniawan ◽  
Antoni Zulius
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Facial Recognition ◽  
Histogram Equalization ◽  
Fingerprint Recognition ◽  
Raspberry Pi ◽  
Security Systems ◽  
Neuro Fuzzy ◽  
Initial Processing ◽  
Artificial Network

One of the biometric technologies that have been implemented in many security systems besides retinal recognition, fingerprint recognition and iris is facial recognition. On the hardware side itself, face recognition (Face Recognition) uses a camera to capture a person's face then compared to the previous face that has been stored in a particular database. There are several methods of facial recognition, namely neural networks, artificial neural networks, adaptive neuro fuzzy, and eigenface. Specifically in this study the method to be explained is the eigenface method. Specifically in this study the method that will be explained is the eigenface method, and uses a web cam to capture images in real time. The advantage of this method is that the computation is very fast and simple compared to the use of methods that require a lot of learning, such as artificial network requirements. Broadly speaking, the process of this application is the camera to capture faces, then an RGB value is obtained. Using the initial processing, resize, RGB to Grayscale, and histogram equalization for light alignment. The eigenface method functions to calculate the eigenvalue, and the eigenvector that will be used as a feature in making recognition. From the experiments and tests carried out, the tool can recognize facial images with a success rate of up to 90% at a distance of 25 cm with an average success of 72.5%. This proves this tool is quite good in face recognition.

scholarly journals Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

2020 ◽  
Vol 8 (3) ◽  
pp. 210-216
Author(s):  
Subiyanto Subiyanto ◽  
Dina Priliyana ◽  
Moh. Eki Riyadani ◽  
Nur Iksan ◽  
Hari Wibawanto
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Principal Component ◽  
Recognition System ◽  
Classification Performance ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security ◽  
Ga Algorithm ◽  
The Face

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

scholarly journals Design of Intelligent Facial Recognition System using AI for Surveillance Application

2020 ◽  
pp. 346-356
Author(s):  
Syed Ibrahim ◽  
Syed Nahid Suleman ◽  
Manikanta Suthapalli ◽  
Abhishek Sharma ◽  
Shilpa K S
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Recognition System ◽  
Security System ◽  
Software Module ◽  
High Security ◽  
Trained Staff ◽  
Facial Recognition System ◽  
Surveillance Application

Organizations presently continue to encounter significant security concerns; consequently, they require much particularly trained staff to achieve the coveted protection. This staff performs blunders that may affect the extent of security. A suggested solution to the matter mentioned above is a Face Recognition Security System, which can monitor and identify trespassers to blocked or high-security areas and assist in overcoming the margin of manual human oversight. This system is comprised of two halves: the hardware part and the software part. The hardware module incorporates a camera, while the software module includes software that uses face-detection and face-recognition algorithms. If a person infiltrates the confine in question, a set of snaps are captured by the camera and dispatched to the software to be examined/identified and equated with an existent database of trusted people. An alert is conveyed to the user if the infiltrator is not recognized.

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