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 Vibration Guided Automatic Vision for Enhanced Security

2019 ◽  
Vol 9 (2S) ◽  
pp. 301-306
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Security System ◽  
Single Shot ◽  
Human Intervention ◽  
Alert System ◽  
Security Systems ◽  
Home Security ◽  
Shot Detection

The existing security systems are secure but are not smart enough to handle arbitrary scenarios leading to many false triggers of the alert system. Furthermore, these systems require constant human intervention which isdifficult to achieve.They are also vulnerable as they contain many loopholesand the sensors used are easily manipulatable. The proposed system tries to solve this problem in an efficient and a smart way by the use of sensors, AI and IoT which makes the system robust and resistant againstattacks. The system implements advanced face detection via Single Shot Detection and face recognition via Inception Neural Network for recognition of object in a fast and accurate way. This helps the system act according to the situation, thus preventing any damage to theregion which implements this system. In this work the proposed system is implemented and tested as a Home Security System. The system can also be extended to work in other areas like banks, data hubs, museums etc.The overall accuracy of the system was recorded to be 97.95%.

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 A Face Recognition Method in the Internet of Things for Security in Smart Recognition Places

2022 ◽  
Vol 10 (1) ◽  
pp. 687-690
Author(s):  
Prof. Kalpana Malpe
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Associate Degree ◽  
Human Life ◽  
Recognition System ◽  
Raspberry Pi ◽  
Living Things ◽  
Face Recognition System ◽  
The Face ◽  
Face Detection And Recognition

Abstract: In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

Face Detection and Recognition Using Raspberry Pi

2021 ◽  
pp. 351-354
Author(s):  
P. R. Dolas ◽  
Pratiksha Ghogare ◽  
Apurva Kshirsagar ◽  
Vidya Khadke ◽  
Sanjana Bokefode
Keyword(s):  
Face Detection ◽  
Raspberry Pi ◽  
Face Detection And Recognition ◽  
Detection And Recognition

scholarly journals Real time Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals РОЗРОБЛЕННЯ МЕТОДИКИ PSMECA АНАЛІЗУ ПРИ ЗАСТОСУВАННІ IoT КОМПОНЕНТІВ У СИСТЕМАХ ФІЗИЧНОЇ БЕЗПЕКИ

2018 ◽  
pp. 63-73 ◽  
Author(s):  
Ahmed Waleed Al-Khafaji
Keyword(s):  
Internet Of Things ◽  
Research And Development ◽  
Theoretical Models ◽  
Security System ◽  
Raspberry Pi ◽  
The Internet ◽  
Physical Security ◽  
Security Systems ◽  
Internet Of Things Environment ◽  
The Internet Of Things

Physical security systems are applied to alert in advance a well-known vector of attacks. This paper presents an analysis of the research and assessment of physical security systems applying the PSMECA technique (analysis of modes, efforts, and criticality of physical security). The object of research and analysis is the physical security system of the Ministry of Education and Science of Iraq (as the infrastructure of the region's objects), as well as the area of the compact living of students and co-workers (campus). This paper discusses the organization of physical security systems, which are based on devices with low power consumption and function in the Internet of things environment. The main aim is to describe and develop a physical security system that functions in the Internet of things environment, as well as the development of a scheme for the research and development of models and methods for risk analysis, models of functions and components, models of failures and conducting research and analysis of occurrence failures of PSS. The generalized structural and hierarchical scheme of the physical security system of the infrastructure of the region is presented, as well as the applied application of the scheme is illustrated by the example of the physical security system of a student campus of one of the universities of Baghdad. The functional modeling scheme of the object is provided and is based on the use of the Raspberry Pi microcomputer and the Arduino microcontroller. The set-theoretical models of functions, components, and failures of the system under study, as well as the projection of a hierarchical failure structure in the table of the basic structural elements of the system, are presented. The IDEF0 diagram, showing a power outage scenario (accidental or intentional) in connection with lighting and video subsystems, is presented. The scheme of research and development of models and methods of analysis of risks of PSS is carried out in the paper. A PSMECA table for the CCTV system has been created, which allows you to more precisely determine the cause of the failure in the physical security system and the importance of failure criticality

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