IoT based smart mirror

Smart appliance design that includes multimedia intelligence to deliver comfortable, convenient, and secure personal services in the home is becoming increasingly crucial in the age of information and communication technology. This research looks at the design and execution of a novel interactive multimedia mirror system called as "smart mirror." The glass that will be used is the foundation of the design of a smart mirror. Two-way glass is suggested because it allows the visuals on the display to be seen more clearly. Our way of life has evolved to the point where making the best use of one's time is critical. Based on user surveys and prototype implementation, we propose the development of an innovative appliance that incorporates interactive information services delivered via a user interface on the surface of a mirror. Our work is based on the assumption that we all check ourselves in the mirror before leaving the house, so why shouldn't the mirror be intelligent? Smart Mirrors will eventually replace regular mirrors, providing users with both mirror and computer-assisted information services as technology improves. Because of the Raspberry Pi microcontroller cards aboard, the devices can connect to the internet, download data from the internet, and show that data on the mirror. Weather data, time and location data, current event data, and user data gathered from web services using a Raspberry Pi 3 microcontroller card are all included in the designed intelligent mirror system. The mirror will light up when the user steps in front of it. When thinking about this project, phrases like Smart Mirror, Interactive services, Raspberry Pi , and Web services come to mind.

Abstract: This project has been developed within the context of a time where every day we see more and more connected devices. The Internet transformed our lives by connecting us more easily to information and other people in the virtual world. Mobile phones then became smartphones and since then this concept has erupted and morphed into the Internet of Things, things that connect us to everyday objects. There is no end to objects that could be made “smarter”, some being more suited to this than others. Mirrors, for example, provide a large surface ideal for displaying information and interacting with it. Most people have mirrors at home so the concept of a smart mirror that you can interact with is attractive and has been fantasized about in many futuristic movies. Smart mirrors, such as Magic Mirror and Home Mirror, have been only developed by people in the Maker community, with varying degrees of interactivity. However, so far, the features of these mirrors have been limited. This final year project describes how a smart mirror was built from scratch using a Raspberry Pi for the hardware and custom software built on top of Raspbian, a Linux distribution. The goal of the project was to create a Smart Mirror device that people could interact with but also to further develop the technology so that it would let you install and develop your own applications for it. Overall, results were good because a higher level of interactivity has been achieved by being able to use voice commands, gestures, and smartphones. A few problems arose in the construction and software side of the project, such as the glass not being reflective enough and the gesture recognition being unreliable, but these drawbacks can be addressed by doing more tests and trials to further develop the Smart Mirror

In recent years, the integration of artificial intelligence has revolutionized human-computer interaction by enabling devices to provide personalized user experiences. This study focuses on the development of a Smart Mirror with Emotion Monitoring, which utilizes advanced facial recognition and deep learning algorithms to detect and classify user emotions in real time. By analyzing facial expressions, the system identifies emotional states such as happiness, sadness, surprise, angry, and neutrality. Based on these detected emotions, the mirror responds dynamically by displaying personalized content, including motivational messages, and aiming to enhance mental well-being. Beyond emotional responsiveness, the smart mirror also functions as a daily assistant, offering real-time information such as weather updates, calendar, and emotion monitoring insights. Its applications extend beyond residential use to sectors such as healthcare, retail, and corporate environments, where emotion-based interactions can improve user engagement and experience. In healthcare, for instance, the mirror can assist individuals dealing with stress or mental health conditions by providing therapeutic interactions. Similarly, in commercial settings, the mirror can be integrated into customer service environments to enhance user satisfaction. The proposed system is built using state-of-the-art computer vision techniques, deep learning models, and IoT integration to ensure accurate emotion detection and seamless functionality. A robust dataset is employed to train and optimize the facial recognition model, ensuring high accuracy in emotion classification. To validate the system’s performance, extensive experiments are conducted, evaluating parameters such as detection accuracy, response time, and user satisfaction. The results demonstrate the system’s effectiveness in providing real-time emotional insights and personalized interactions, contributing to the advancement of intelligent assistive technologies. By incorporating emotional intelligence into smart mirror technology, this study bridges the gap between functionality and user-centric design, paving the way for next-generation AI-driven smart devices

In recent years the advancement of technologies of information and communication (technology ICTs) have helped to improve the quality of people's lives. The paradigm of internet of things (IoT, Internet of things) presents innovative solutions that are changing the style of life of the people. Because of this proposes the implementation of a smart mirror as part of a system of home automation, with which we intend to optimize the time of people as they prepare to start their day. This device is constructed from a reflective glass, LCD monitor, a Raspberry Pi 3, a camera and a platform IoT oriented cloud computing, where the information is obtained to show in the mirror, through the consumption of web services. The information is customizable thanks to a mobile application, which in turn allows the user photos to access the mirror, using authentication with facial recognition and user information to predict the news to show according to your profile. In addition, as part of the idea of providing the user a personalized experience, the Smart Mirror incorporates a news recommendation algorithm, implemented using a predictive model, which uses the algorithm, naive bayes.

The smart mirror, which gives both the functionality of a mirror and the smart capabilities that come with in front of the users, will have a significant impact on future technology. Such cutting-edge technology would have a profound impact on how people live their daily lives. In recent years, everything has improved in intelligence, including homes, cities, and smart phones. The smart mirror is one of the gadget that utilising a common mirror as a smart device. These mirrors are a sophisticated interface for content screening and can also be used in a cosy atmosphere for intruder detection. The mirror can operate in two different modes: regular mode and smart mode, in which it responds to commands from outside the system and demonstrates relevant information. The smart mirror may be made using the Raspberry Pi-3 model, which also has voice controls and touch screen capabilities.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

Our review paper highlights the design and creation of voice assistance and facial recognition smart glasses using raspberry pi for the benefit of those who are blind or visually impaired. It helps them recognize everyone around them even when their known person is silent. Also, it includes some excellent features like web browsing and songs playing. This idea makes their life simpler and works as a digital assistant. This device has the feature of recognize the face of that people or that object which is already inserted in our system. It takes our face as an input and it provide the output by speaking. We can implement our software by using Raspberry PI, it is also a extremely powerful ARM based system on a chip (SOC). The Raspberry Pi can execute Python and C++ programming and do graphical calculations using its built-in graphics. In this project we have used Raspberry pi 4 face recognition, speaker as a buzzer, google text to speech, Ultrasonic sensor for object detection and ESP 32 camera web server. Our suggested system is superior, extremely effective, extremely affordable, user-friendly, and light weight.

In this paper, we present a unique microprocessor-based wearable glass security system for human beings who are blind or visually impaired. People who are visually impaired face hard to communicate efficaciously with their environment. They always depend on other people. Sometimes they fall into a lot of troubles when on foot in the road, field, marketplace, bus stand, railway station and so on. So, making sure their safety is very important to us. Imparting the blind person the safety information about their surroundings and family members are the goals of the smart glass safety system. This paper offers a correct and dependable real-time monitoring system using global positioning system (GPS). This smart glass device will help blind humans attain improved independence and freedom in the indoor and outdoor environment. We have proposed a smart glass application to address the safety threat to blind people and give security information to the family members of the blind man. The proposed prototype system is based on a Raspberry pi 3 model B and NEO-6M GPS module. Our proposed system is good, low cost, highly efficient and user-friendly.

We introduce a concept for an IoT semi-electronic display device which can detect the users emotions to identify initial signs of depression in the elderly. Such a screening process is expected to appear in future smart healthcare systems. This device functions as a smart mirror which displays daily information such as the weather, events, headline news, currency rates, stocks, and reminders. Elderly users can view information on their mental health while dressing themselves in front of the mirror. The information on their daily emotions will be collected to monitor their health long-term as a nurse or caretaker in their house would do. Moreover, to observe the elderly users emotions, we incorporate additional systems such as a chat bot, facial recognition, voice/speech recognition, and posture recognition. The device also serves as a communication tool that facilitates telemedicine, helping the elderly users at home feel more connected with their doctors. This helps doctors diagnose, follow up, and treat their patients long-term.

At present, face recognition has been widely used, and face recognition in mobile devices has a broad application prospect. We try to detect and extract faces by extracting Haar-like features and then put the face images into the Convolutional Neural Network (CNN) for training and recognition. We improve the MobileNetV2, use ArcFace for face recognition and classification, and test the CNN face classification program on Raspberry Pi 4B. The training in Raspberry Pi is based on the idea of transfer learning. The features extraction part of the network loads the weight file, and only the fully connected classification layers are trained. Finally, the accuracy of train set and test set reach 99.8% and 94.4% respectively after training 100 epochs in Raspberry Pi. When epoch=10, the accuracy rate reaches the predetermined value. We can consider training 10 epochs in Raspberry Pi, and the total time is about 1h33min.

Our research describes the design and development of raspberry pi based smart glasses for the welfare of visually impaired people with face recognition and voice assistant. This idea helps them to recognize people around them even when their known person is not talking and also it has some cool features like playing songs, browsing etc. This makes their life easier and acts as a digital assistant. This device also has Hot word detection. To use this device, The user is requested to call the device using the hot words like "Hello iris", "ok iris" etc. and provide instruction. It takes their voice instruction as input and provides output by speaking. Our software is made to interact with the user like Alexa and google assistant. Using pre built python modules, it is easy for us to add many other features too. Our software can be implemented using Raspberry pi which is a very powerful ARM based SOC (System on a Chip). Raspberry pi can run python, C++ codes and it can also perform graphical calculation with its embedded graphics. In this system, we use Raspberry Pi Face Recognition, ESP 32 camera Web Server, face encodings, Pico voice console, porcupine engine, Google Text to Speech. Our proposed system is better, highly efficient, highly economical, User friendly and low weight.

This paper presents a groundbreaking development in assistive innovation through the improvement of visionary smart glasses custom-made for people with visual impedances. These smart glasses are equipped with a Raspberry Pi Camera module designed to capture images of printed materials such as daily papers, books, and other objects of interest. The captured visual information is then handled through advanced optical character recognition (OCR) technology, changing over it into text. Hence, the text information is consistently transformed into audible content using text-to-speech (TTS), which the user can tune in to through an associated headset.The main objective of these visionary smart glasses is to enable the visually challenged by giving real-time access to printed materials and improving their ability to independently engage with composed data. Furthermore, these smart glasses incorporate a second module outlined for cash reading, permitting users to precisely distinguish and manage distinctive categories of currency. This dual-functionality aims to extend availability and independence, subsequently cultivating more prominent inclusivity and quality of life for individuals with visual impairments.The advancement of these visionary smart glasses represents a critical step towards bridging the data gap that frequently hinders the visually challenged community. By consistently converting printed materials into audio and empowering quick and reliable cash identification, these inventive smart glasses are poised to revolutionise the way people with visual disabilities interact with their environment, eventually enhancing their freedom and overall well-being. Keywords— Envisioning, Revolutionizing, Raspberry Pi, Camera Module, OCR, TTS, Currency.

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

Smart devices are exciting and upcoming advancements in the field of Internet of things (IOT). It is an application of Raspberry Pi, from furniture in the residential and commercial sector to everyday devices like watches and headphones, every object is becoming smarter and hence, making human lives much easier and efficient. Users can actively interact with these devices using voice commands. These smart devices can also assist a user in their personal daily activities as well as in medical use. Currently, various variations of the smart mirror based on software and hardware compatibility as well as based on applications and uses are available in the market. The goal of this paper is to understand the working, various applications, and purposes of the smart mirror according to the varying features in different versions. This paper critically analyses the different versions of the smart mirror and its different features and applications.

This study used smart glasses as the carrier to develop three applications aimed at the elderly. Taiwan entered an elderly society in 2018, with the proportion of the elderly population exceeding 14%. It is estimated that the elderly population will exceed 20% in 2026, and Taiwan will enter an ultra-elderly society. To improve the quality of life of the elderly and attach importance to the health care of the elderly, more and more scientific and technological products are used to improve the life and consumption patterns of the elderly, thereby promoting the popularization of smart wearable devices and sensing technologies. In view of the purpose, this research develops related functional applications with smart glasses. First, the memory recall mechanism is carried out based on face recognition. When the user uses the function, an embedded camera in glasses is turned on to capture the target personnel. Afterwards, a KNN (K Nearest Neighbor) machine learning approach is applied to identify the target, and the personal data is performed in the glasses through searching on the developed cloud database. Besides, the user can add, delete and modify the personal data through the computer, which the entrance is carried out by scanning the two-dimension code shown in the computer program with glasses for security purpose. Secondly, the image enhancement function shows that the captured texts can be expanded and enlarged in the projection monitor of glasses. Finally, the construction of voice commands is performed through the microphone within the suite of smart glasses. The results show that the accuracy rate of KNN face recognition is 93.3%, which can be applied to general life situations.