Smart Glasses Embedded with Facial Recognition Technique

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 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.

Nowadays, people have a very busy and hectic life; thus, taking care of an infant constantly is tough. Especially for working parents, a modernized IoT-enabled baby management system will be beneficial. In these modern times, most children are taken care of by their grandparents, housemaids, or babysitters during the daytimes. In this work, we are using a very efficient and user-friendly technology to implement automatic swinging of the baby bassinet with sound detection of the baby crying using sound sensor and playing lullaby through speakers. The humidity sensor has been used to know the diaper's moisture level, and notifications have been sent to parents with certain conditions through mobile calls and text messages. A webpage using HTML and CSS has been developed, where parents/guardians can supervise the baby in real-time. Finally, the system will detect if the baby is in the cradle using the face recognition technique. This exciting feature is implemented by using a Raspberry Pi 4 (Model B), which is equipped with a Pi camera that will also offer the parents a live-stream option. The proposed baby monitoring system is believed to be an improved technology for new and working parents and colossal help and riddance of unrequired tensions.

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.

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.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

Preparing in front of the mirror in the morning takes time. This Smart Mirror can solve several business issues simultaneously. This voice service system analyzes customer inquiries and instructions using "ALEXA". Smart Mirror, based on the Raspberry Pi 4, is the newest design to replace our mirrors with high-tech and inventive applications. We have all seen various things that help the country and world thrive in this modern era. Multitasking is required since it is hard to fit everything into a day. The challenge is controlling everything that might impact a person to prepare for each day and accomplish all the critical chores in front of the mirror more efficiently.This project aims to construct a smart mirror that provides general information like news, time, weather, and other valuable data to the general public. This mirror collects this information throughout morning preparation to make it easier. Our items can incorporate music, control techniques, and other entertainment to make this mirror more fascinating. Smart glass can improve a modern lifestyle. Face recognition will enhance mirror use.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

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.

Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins

Nowadays, closed circuit television for security and peace purpose for people. The traditional system has the several disadvantages such as inconvenient to carry, anomalies cannot be detected, storage space needed is more, and cost remains high. This paper provide the design and implementation of the technology called Live video streaming using Raspberry Pi in IOT devices, with a single board computer which computes the Motion Detection Algorithm written in python as programming environment. The system uses the algorithm to significantly decrease the storage space and to save the cost. The algorithm is implemented on the Raspberry Pi, which provide the live streaming with motion detection. The live steaming can be viewed from any web browser or even from mobile in the real time.

Due to Covid-19, there are currently no effective face mask detection applications, which are in high demand for transportation, densely populated areas, large-scale manufacturers, residential districts, and other organizations to assure safety. Also, the absence of large datasets of images with mask has made task more cumbersome and challenging. This project proposes a method for identifying people without wearing using face recognition technique by using python programming and from Open CV library, Keras and tensor flow. Standalone embedded device has been developed by using Raspberry Pi electronic development board and can be operated on Battery power supply, wireless internet connectivity by using USB modem. Automatic e-mail notification has been achieved by sending security alert mail to the user e-mail id when the person without mask tries to enter the premises. This proposed system is more effective, reliable, and this system consumes very less data and power compared to the other existing systems.

Deep CNN models have become state-of-the-art techniques in many application, e.g., face recognition, speaker recognition, and image classification. Although many studies address on speedup or compression of individual models, very few studies focus on co-compressing and unifying models from different modalities. In this work, to joint and compress face and speaker recognition models, a shared-codebook approach is adopted to reduce the redundancy of the combined model. Despite the modality of the inputs of these two CNN models are quite different, the shared codebook can support two CNN models of sound and image for speaker and face recognition. Experiments show the promising results of unified and co-compressing heterogeneous models for efficient inference.

Face Recognition (FR) is a significant area in computer vision plus pattern recognition. The face is the easiest mode to discriminate the specific individuality of every other. FR is a particular identification scheme that usages particular features of an individual to recognize the individual's identity. The challenges in FR are aged, facial terms, variations in the imaging surroundings, illumination plus posture of the face. Specially, in this study firstly we mark an outline of FR that includes definition, types and problems. Secondly, we provided a complete related work of FR. The objective of this study is to provide a comprehensive outline on the work that has been carried out over the previous spans in the progressing area of FR. This study offers an extensive view of theories, methodologies, up-to-date techniques for FR.Â