scholarly journals Smart Mirror Using Raspberry PI: A Review

2022 ◽  
pp. 37-43
Author(s):  
Saish Bavalekar ◽  
Ninad Gaonkar
Keyword(s):  
Daily Life ◽  
Facial Recognition ◽  
Raspberry Pi ◽  
Daily News ◽  
Central Controller ◽  
Cloud Servers

A Smart mirror is a mirror with technology integrated with it. It uses a two-way mirror and has an inbuilt display at the back showing us different information in the form of widgets about the date, time, temperature, daily news updates. The Raspberry Pi acts as the central controller, which powers the display and collects data through sensors. The data collected is stored on cloud servers for further use. The mirror comes with facial recognition technology, which helps authenticate the user every time the user comes in the mirror range. With the help of voice commands, the mirror application can be queried to get the desired data. This automation has helped in multitasking which strives to optimize time in our daily life. In this manuscript we will review different applications of smart mirror.

scholarly journals COVID-19 Authorized Entry with Face Mask Detection using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 1862-1867
Author(s):  
Anju Ajay
Keyword(s):  
Large Scale ◽  
Facial Recognition ◽  
Face Mask ◽  
Raspberry Pi ◽  
Wireless Internet ◽  
Great Demand ◽  
Embedded Device ◽  
Internet Connection ◽  
Battery Power ◽  
Python Programming

There are no effective face mask detection applications in the current COVID-19 scenario, which is in great demand for transportation, densely populated places, residential districts, large-scale manufacturers, and other organizations to ensure safety. In addition, the lack of big datasets of photographs with mask has made this task more difficult. With the use of Python programming, the Open CV library, Keras, and tensor flow, this project presents a way for recognizing persons without wearing a face mask using the facial recognition methodology. This is a self-contained embedded device that was created with the Raspberry Pi Electronic Development Board and runs on battery power. We make use of a wireless internet connection using USB modem. In comparison to other existing systems, our proposed method is more effective, reliable, and consumes significantly less data and electricity

scholarly journals Visual Assistant for Blind People using Raspberry Pi

2021 ◽  
pp. 671-675
Author(s):  
Tejal Adep ◽  
Rutuja Nikam ◽  
Sayali Wanewe ◽  
Dr. Ketaki B. Naik
Keyword(s):  
Visually Impaired ◽  
Daily Life ◽  
Artificial Vision ◽  
Raspberry Pi ◽  
Blind People ◽  
Python Language ◽  
Visually Impaired People ◽  
Impaired People ◽  
Visual Aid ◽  
Impaired Person

Blind people face the problem in daily life. They can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision-based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects and signboards. This will help the visually impaired person to manage day-to-day activities and navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

scholarly journals MULTI-FACTOR AUTHENTICATED SECURE DIGITAL LOCKER USING GSM

2021 ◽  
Vol 7 (2) ◽  
pp. 7-12
Author(s):  
Anshita Dhoot ◽  
◽  
Kristi Shumka ◽  
M. Salman Saeed ◽  
A.N. Nazarov ◽  
...  
Keyword(s):  
Elevated Level ◽  
Daily Life ◽  
Facial Recognition ◽  
Sensitive Information ◽  
Physical Security ◽  
Developing Trend

In this time and age, technology is on an ever-developing trend, bringing with it a plethora of new advancements to daily life and common activities, but with these advancements, a whole new set of issues and vulnerabilities become present. This paper aims to discuss and propose a solution to the vulnerability of physical security lockers, a facet of security that many organisations tend to neglect. It aims to provide an elevated level of security for companies who use physical lockers to secure sensitive information or important contents. More specifically, the paper discusses the plausibility and the advantage of utilising multi-factor authentication with dynamic passwords or OTP code for short and facial recognition to access physical lockers. The proposed system will require employees to have accounts in their respective locker by entering their respective ID and passing the facial recognition feature. The system will then provide the employee with an OTP code, allowing the company an extra layer of security from intruders or malicious individuals.

scholarly journals Smart Mirror

2019 ◽  
Vol 8 (2S11) ◽  
pp. 925-929
Keyword(s):  
Visual Representation ◽  
Raspberry Pi ◽  
Home Automation ◽  
Proximity Sensor ◽  
Daily News ◽  
Turn On ◽  
Wide Range ◽  
Personal Assistant ◽  
Temperature Time

This paper describes and explains the design and working of a mirror made smart called Smart mirror built using raspberry pi. The visual representation of the product is similar to a regular mirror that can display weather details, temperature, time and daily news on voice commands. The smart mirror acts as a personal assistant, an enquiry center and displays important curriculum or college notices. It can answer basic questions, display class timetable and show directions to various places of an institute. It could be linked to google cloud for various IOT based tasks like home automation etc. It uses a proximity sensor to turn on the screen if in use and turn back off when not in use. Built on a strong speech to text engine, it understands various types of accents to understand predefined command clearly. The smart or interactive mirror designed by using a raspberry pi as the main controller and a led display that is placed behind the mirror, can serve a variety of endless application-based commands. With such a wide range of applications, this could certainly become an important part of technology in future times.

scholarly journals Facial recognition enabled smart door unlock system

2018 ◽  
Vol 7 (2.7) ◽  
pp. 183
Author(s):  
M Vamsi Krishna ◽  
A Bhargav Reddy ◽  
V Sandeep
Keyword(s):  
Daily Life ◽  
Facial Recognition ◽  
Normal Person ◽  
Sliding Method ◽  
Web Camera ◽  
The Face ◽  
Entire Society ◽  
The Voice ◽  
Display Monitor ◽  
The Internet Of Things

To verify that our daily life is going in a secure way. Lot of research programmers are going on in this entire society. The turning point comes through the internet of things, industry has been emerged with the lots of elements provided from IOT. We can able to connect our daily life things or objects with this had successfully evolved lots of things.  This Facial recognition door unlock system is a process is which will detect the face and identifies the among people. People are having different types of face cut, in that particularly there are many unique faces which are different from each other which inspired us, from that concept this process has been established. Our main aim to create the smart door system to a house, that will secure the house and all your personal things at your home. In this concept of our system we have been used alive web camera in the front side of the door, along with the display monitor. this web camera shows the owner/particular viewer the whom the house is his control, this shows the person who stood front of the door, the system is setup the voice output is being processed by the processor that which is used to show the answers/instructions as the output on the screen. We are using a stepper motor that which is used to lock/open then the by sliding method, so that a normal person stand in front of the door and access it. This process is done through this Microsoft face API application. The display is being operated on a Microsoft Visual Studio application.

scholarly journals Study of Impact of Computer Vision in Detecting Human Emotions

2021 ◽  
Vol 10 (11) ◽  
pp. 82-83
Author(s):  
Ravindra Kumar ◽  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Facial Expression ◽  
Facial Expressions ◽  
Daily Life ◽  
Facial Recognition ◽  
Emotion Identification ◽  
Human Emotions ◽  
And Behaviors ◽  
Using Data

Emotions play a powerful role in people's thinking and behaviors. Emotions act as a compulsion to take any action and can influence daily life decisions. Human facial expressions show humans share the same set of emotions. From the setting, the concept of emotion-sensing facial recognition was brought up. Humans have been working actively on computer vision algorithms, the algorithm will help determine the emotions of an individual and can determine the set of intentions accompanied by the emotions. The emotion-sensing facial expression computers are designed using data-centric skills in machine learning and can achieve their desired work by emotion identification and a set of intentions related to the emotion obtained.

scholarly journals Smart Raspberry Pi Bank Safety Deposit box With Facial Recognition: Fintech Case Study

2020 ◽  
Author(s):  
Jay Patel ◽  
Sabah Mohammed
Keyword(s):  
Facial Recognition ◽  
Hall Sensor ◽  
Raspberry Pi ◽  
Case Scenario ◽  
Secure Storage ◽  
The Face

<p>Bank safety deposit box is one of the secure storage for the clients to store their valuables such as important property or business paper, gold or even money but is in some case scenario traditional safe deposit are not safe enough, there have been many robberies from this safe box because of the code lock can be hacked and there no alarming system in this kind of safe boxes. In this paper I proposed a smart secure safe box with facial reorganization unlock feature using raspberry pi and uno Arduino which can used to secure your documents or valuables in a bank or at your home. In this I have used raspberry pi and pi camera to perform the face unlock and to send security alert through email. Secondly, I have used Arduino and hall sensor to trigger an alarm when the door is open unauthorized or kept open.<i></i></p>

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.

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