Development of Face Recognition on Raspberry Pi for Security Enhancement of Smart Home System

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.

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Smart Door for Handicapped People via Face Recognition and Voice Command Technique

Engineering and Technology Journal ◽

10.30684/etj.v39i1b.1719 ◽

2021 ◽

Vol 39 (1B) ◽

pp. 222-230

Author(s):

Hana'a M. Salman ◽

Rana T. Rasheed

Keyword(s):

Face Recognition ◽

Smart Home ◽

High Performance ◽

Cost Effective ◽

Recognition System ◽

Raspberry Pi ◽

Face Recognition System ◽

The Face ◽

Handicapped People ◽

Facial Identification

Smart home indicates an application for different technological implementations, it could indicate any system which controls the door lock and several other devices. Facial identification which is an important section to achieve surveillance and safety, especially for handicapped people, can be considered as one of the ways that deal with biometrics and performed to identify facial images via utilizing fundamental features of the face. A Raspberry Pi-based face recognition system using conventional face detection and recognition techniques is going to be supplied, so the method in which image-built biometrics uses a Raspberry Pi is described. The aim of the paper here can be considered as transferring face recognition to a level in which the system can replace the utilizing of RF I-Cards and a password to access any system of security and making the system alive and protect the door from being open by hackers, especially by using the picture of an authorized person, we make the raspberry pi turn off and cannot turn on only by a command from the authorized person's mobile. The result of the presented proposal is a system that uses face recognition by utilizing OpenCV, Raspberry Pi, and it functions on an application of Android, and this system percentage becomes 99.63%. It should be cost-effective, of high performance, secured, and easy to use, which can be used in any smart home application.

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Conception and implementation of a course for professional training and education in the field of IoT and smart home security

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.3.mobmu-276 ◽

2020 ◽

Vol 2020 (3) ◽

pp. 277-1-277-8

Author(s):

Michael Pilgermann ◽

Thomas Bocklisch ◽

Reiner Creutzburg

Keyword(s):

Smart Home ◽

Professional Training ◽

Raspberry Pi ◽

Science Students ◽

Training And Education ◽

Home Security ◽

Master Level ◽

University Course ◽

Home Technology ◽

Smart Home Technology

The aim of this paper is to describe the new concept of a Master level university course for computer science students to address the issues of IoT and Smart Home Security. This concept is well suited for professional training for interested customers and allows the creation of practical exercises. The modular structure of the course contains lectures and exercises on the following topics: 1. Introduction - IoT and Smart Home Technology and Impact 2. Homematic Technology and Smart Home Applications 3. Loxone Technology and Smart Home Applications 4. Raspberry Pi and Smart Home Applications 5. Security of IoT and Smart Home Systems and contains laboratory exercises of diverse complexities.

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Face recognition in an intelligent door lock with ResNet model based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189624 ◽

2021 ◽

pp. 1-11

Author(s):

Suphawimon Phawinee ◽

Jing-Fang Cai ◽

Zhe-Yu Guo ◽

Hao-Ze Zheng ◽

Guan-Chen Chen

Keyword(s):

Face Recognition ◽

Detection System ◽

Low Cost ◽

Ease Of Use ◽

Raspberry Pi ◽

Training Methods ◽

Graphics Recognition ◽

Face Images ◽

The Face ◽

Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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Design of multifunctional Android-Based smart home control and monitoring system using Raspberry Pi

10.1063/5.0074133 ◽

2021 ◽

Author(s):

Divya Durgam ◽

Srilatha Talluri

Keyword(s):

Monitoring System ◽

Smart Home ◽

Raspberry Pi ◽

Control And Monitoring System

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Face Recognition Using Raspberry PI

Studies in Computational Intelligence - Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough ◽

10.1007/978-3-030-38445-6_1 ◽

2020 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Shruti Ambre ◽

Mamata Masurekar ◽

Shreya Gaikwad

Keyword(s):

Face Recognition ◽

Raspberry Pi

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Fog Computing in the context of Smart Home, voice assistant and the future of IoT

Revista Colombiana de Computación ◽

10.29375/25392115.3894 ◽

2020 ◽

Vol 21 (1) ◽

pp. 6-12

Author(s):

Javier Pinzón Castellanos ◽

Miguel Antonio Cadena Carter

Keyword(s):

Cloud Computing ◽

Distributed Computing ◽

Data Transmission ◽

Smart Home ◽

Fog Computing ◽

Relevant Information ◽

Raspberry Pi ◽

Thermal Sensors ◽

Computing Architecture ◽

The Future

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

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