Access Control Using Facial Recognition

This research paper talks about the development of an innovative IoT-based smart door lock that also uses facial recognition, Internet of Things (IoT) integration, and the ESP32-CAM microcontroller to increase home security. As locks continue to evolve with more new features, the use of IoT is increasing and enhancing the security and usability. ESP32-CAM was selected for this project because of its built-in Wi-Fi connectivity and camera, which render it exceptionally suited for creating a smart lock system that is able to not only recognize the saved faces but also alert the user if there is an intruder. When compared to other microcontrollers like Raspberry Pi, the ESP32- CAM offers many more advantages, including enhanced security features, a userfriendly interface, and a lower cost, and much more in initial price. This paper also compares and talks about already existing facial recognition-based door locks, providing a detailed analysis of their plus points and limitations to understand the current state of the technology. The research also provides a detailed breakdown including the system's development process, selection of components for the device, and compilation of hardware components for the implementation of the model, The paper also includes the algorithms and user interface design. In this paper, you’ll see how each component works, its usage, and how each component works together to ensure reliable and accurate facial recognition. Also, model testing was done to get the device's performance in a number of different conditions and usage areas. The results of these tests show that ESP32-CAM based smart door lock is a good and powerful solution and it is also cost-effective, offering good amount of savings compared to other smart lock systems in the market. This paper highlights how the project talks about some of the common security problems associated with the old locking system. By offering a more accessible and reliable alternative, this smart lock system has the potential to revolutionize home security. The integration of advanced facial recognition technology with IoT makes the home door lock system a more secure and user-friendly approach to home access control.

Home automation system is a technological solution. It is the action of applying technology to solve a problem at home. It uses hardware and software technologies that enables control and management over appliances and devices within a home. In this paper, we have developed a low-cost automated door lock system so users can operate systems from anywhere all over the world with internet access. IoT concept is applied in the system development including integration of software and hardware for communication and execution tasks. By using a WIFI enabled microcontroller and connecting devices and user’s apps via MQTT protocol, a smart door lock using IoT application has successfully built. The door lock is able to lock and unlock remotely. The users do not need to be on the same network to execute the operation and instruction is conducted via voice command or manually operated on dashboard. The users also will receive feedback as verification for the status of the door when execution take place.KeywordsIoTAutomationMicrocontrollerMQTT

In recent years, smart door locks having password keyboards, fingerprint recognition and communicating function have seen explosive growth. A large number of enterprises engaged in the manufacture of the intelligent door locks, followed by the associated after-sales problems, and the product without power, no reaction phenomenon caused by the failure of master control chip is relatively common, which greatly affects the user experience. In this paper, a mainstream smart door lock products on the market as the research object, a series of failure analysis and root cause search were executed to find the reasons of failure. The research shows that the main reason for the non-power supply and non-response of the intelligent door lock after sale is the fault of the main control chip. The main control chip fails due to the lack of electrostatic protection, which is directly related to the improper production and assembly process.

As smart technology use is growing in residential environments, research on howsuch technologies can provide opportunities for safely and actively aging inplace by integrating physical activity into daily routines and reducingsedentariness is scarce. This study investigated older adults’ intentions to useand attitudes toward currently available smart home technologies that couldcontribute to safe and active lives in and around home. The focus was on fourrepresentative technologies: smart lighting, smart door locks, smart fireprevention devices, and smart home systems/home automation. This paper presentsthe results of a sequential mixed-methods study comprised of online andin-person surveys (n = 129), and a focus group ofcommunity-dwelling older adults, aged 50+ (n = 15). Ordinalregression analyses indicated that perceived usefulness consistently predictsolder adults’ attitudes and willingness to use smart home products. While smartfire prevention devices were viewed most favorably due to their potential safetybenefits, perceived affordability significantly influenced older adults’intentions to use them in their homes. The focus group findings underscoretechnology skepticism, privacy concerns and return on investment as significantdeterminants of attitudes toward the smart design products. The study hasimplications of designers and manufacturers by providing insights on how toprioritize smart home technology integrations to homes.

Smart home security and safety systems have gained more importance in recent years. This is attributable to their significant impact in reducing and preventing loss of assets and human life. The COVID-19 pandemic adds a new dimension to home security as potentially infected people or those not taking necessary precautions such as sanitization or wearing masks may enter homes and cause further transmission. The traditional security systems are effective in conventional scenarios but require human intervention and contact which can lead to the spread of the virus. This necessitates the development of smart security systems that are autonomous and contactless. This paper presents a novel IoT enabled home security system that restricts unauthorized access and at the same time ensures that permitted users are normothermic and are following proper COVID hygiene. The proposed system is a smart edge device that does not require a cloud platform for its computational needs. Facial recognition is used to authenticate and allow approved users to get access. If an unfamiliar person tries to enter the premises, the system takes a photo, computes mask detection on the image, measures temperature and sends these three data points as a WhatsApp notification to the administrative user. The door lock can then be controlled automatically or remotely using a WhatsApp bot.

The widespread adoption of consumer electronics (CE), particularly in smart home ecosystems, has led to the proliferation of intelligent devices such as smart cameras, video doorbells, and smart door locks. While these devices enhance convenience and automation, they generate massive amounts of visual data containing sensitive personal information, thereby increasing the risk of privacy leakage and challenging traditional encryption methods in consumer IoT networks. To address this issue, a cryptanalysis-driven image encryption scheme via a chaos-based 3D S-box and genetic code is proposed, specifically designed to safeguard visual privacy in consumer applications. First, a novel dynamical architecture, formally termed 3D Non-degenerate Hyperchaos (3D-NDHC), is constructed to serve as a robust entropy source. Explicitly defined as a globally bounded volume-expanding hyperchaotic system, 3D-NDHC distinguishes itself from traditional dissipative maps by exhibiting global robust hyperchaos and scalable complexity, where the Lyapunov exponents grow logarithmically with control parameters, ensuring high unpredictability. Next, a dynamically generated three-dimensional S-box driven by these chaotic sequences is introduced to perform efficient image permutation. Subsequently, a genetic code method enhanced by cryptanalysis is applied to confuse the permuted image matrix, specifically addressing the security vulnerabilities often inherent in lightweight encryption algorithms. Finally, under the control of pseudo-chaotic sequences, a lightweight wave diffusion process is employed to spread the pixel values and produce the final ciphertext image. In contrast to conventional algorithms that rely on standard encryption techniques, the proposed approach integrates a chaos-driven 3D S-box with a cryptanalysis-enhanced genetic code scheme. This combination introduces a high level of confusion and diffusion, significantly increasing the difficulty for attackers attempting to illegally decrypt sensitive images from smart devices. Experimental results and security analyses demonstrate that the scheme effectively resists various cryptographic attacks and outperforms several recently proposed image encryption algorithms, offering a robust solution for protecting digital image privacy in smart home environments.

Facial recognition system is a system developed by many technology companies. Face recognition has become a system that is widely used for various purposes such as the development of security systems, identification of identities, as well as processing of an image or film. One of the methods used to create this system is to use the ESP32 CAM module which is connected to the Arduino, where the ESP32 CAM is a module with an OV2640 camera sensor that can be used to take pictures and face recognition. ESP32 CAM is a development of the previous module and has an ESP32 chip with dual connectivity namely WiFi and Bluetooth. The face which is a part of the human body that is often the focus of attention in social interactions because it plays a vital role by showing identity and emotions. Will be used as an indication of the introduction of a person or Face Recognition. The basic principle is that by quoting the face's unique information, the system will read and recognize it. Face recognition is a pattern recognition (pattern recognition). This can be described as a classification of a face whether recognized or not recognized, and once recognized, the face pattern will be stored. The ESP32 CAM that has been connected to the Arduino will detect whether the face is recognized or not, then the data will be sent to the relay and if the face is recognized, the door lock will open automatically, while if the face data is not recognized, the door lock will not open.

Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.

Emotion recognition using artificial intelligence (AI) has become an increasingly vital area of research, offering transformative applications across healthcare, human-computer interaction, marketing, education, and entertainment. This paper provides a comprehensive review of the techniques and applications of AI in emotion recognition. Key methodologies, including facial expression analysis, speech emotion recognition, text-based emotion detection, physiological signal analysis, and multimodal approaches, are explored in detail. Each technique's underlying algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP) methods, are examined for their efficacy in identifying human emotions. This paper also delves into practical applications, showcasing how emotion recognition enhances mental health monitoring, improves user experience, optimizes customer service, personalizes education, and enriches entertainment media. Moreover, this study addresses the ethical and technical challenges associated with emotion recognition, such as data privacy concerns, potential biases in AI models, and the need for accuracy and reliability in emotion detection systems. The discussion extends to future directions, emphasizing the integration of AI with wearable devices, advancements in multimodal systems, and the expansion of emotion recognition technology into new domains. By presenting a thorough analysis of current techniques and applications, this paper aims to highlight the significant impact and potential of AI-driven emotion recognition, paving the way for future innovations and ethical considerations in this dynamic field. The proliferation of Internet of Things (IoT) technology has enabled the development of smart home systems that can enhance home security, energy efficiency, and convenience. This paper presents the design and implementation of an IoT-based smart home security system that utilizes a variety of sensors and actuators to monitor and control various aspects of home security. The proposed system integrates multiple components, including motion detectors, door/window sensors, cameras, smart locks, and a central control unit, all interconnected through a wireless network. The system leverages cloud computing and machine learning techniques to process sensor data, detect potential threats, and initiate appropriate responses. A user-friendly mobile application allows homeowners to monitor and control the security system remotely. The paper discusses the system architecture, hardware and software components, communication protocols, and security considerations. The implemented system demonstrates the feasibility and potential benefits of IoT-based smart home security solutions.

Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the employment of such systems in the context of a smart home can be challenging due to the devices hardware limitations, which may restrict their ability to counter the existing and emerging attack vectors. Therefore, this paper proposes an experimental comparison between the widely used open-source NIDSs namely Snort, Suricata and Bro IDS to find the most appropriate one for smart homes in term of detection accuracy and resources consumption including CP and memory utilization. Experimental Results show that Suricata is the best performing NIDS for smart homes

Smart Home is one of the tools that is developed for ease of automation of smart home management from the start of address, security, comfort, savings, through automation with Android. In designing a micro home security system controller using raspberry pi 3 and Android smartphones that can reduce the number of criminal acts of burglary door. This tool consists of an electro magnetic door lock called a solenoid door lock.This solenoid key is placed on the door of the house for security. The design of this home door security system utilizes Raspberyy pi b + as a control device from near and far by utilizing the wifi network and sms gateway to control opening and closing the home door lock that is controlled via an android mobile. Through web bootstrap that will display the results captured by the camera to provide a home situation every time someone enters.This house door security system that has been successfully built and tested with the working principle if there is someone who forces or breaks the house door in a closed condition, the system will activate a warning or alarm by sounding the buzzer¸ because there is an LDR sensor connected to one switch connected to the solenoid key that results, if the key is opened with a security system then the LDR sensor will turn off and there will be no alarm, but if it is forced to break the LDR sensor will activate and read the movement of the door so that the reaction occurs and the buzzer alarm will sound. This security system is a solution to increase the level of home security ¸ besides this sophisticated system is very easy to use and integrated with android smartphones

Real-time face detection and recognition using hybrid-information extracted from face space and facial features

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.

Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home’s physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios—asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms’ consistency and concurrency intricacies.

Recently, there are a large number of smart lock systems that can be used in the workplace or home, but many of them depend on finding an external key, password, fingerprint, and facial recognition, where these systems suffer from weakness as the physical key may be forgotten in a place, the password can be memorized which is hard for some peoples, and the fingerprint system sometimes does not work on people who suffer from chronic diseases, such as people with diabetes and for each of these systems the person needs a free hand so that he/her can open the lock which means that is if the person carries something he/her needs to be placed somewhere. The facial recognition system cannot recognize the person's face when the place has poor lighting. This paper proposes a home door lock system that relies on the internet of things (IoT) technology and portable electronic devices Bluetooth such as a smart mobile using the device Bluetooth media access control (MAC) address as a key and the IoT for monitoring, which leads to a door lock that does not require a free hand. The proposed lock system outcome can be a good candidate for home security applications.