Access Control, Biometrics, and the Future

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

The increased complexity of modern access control (AC) systems stems partly from the need to support diverse and multiple administrative domains. Systems engineering is a key technology to manage this complexity since it is capable of assuring that an operational system will adhere to the initial conceptual design and defined requirements. Specifically, the verification stage of an AC system should be based on techniques that have a sound and mathematical underpinning. Working on this assumption, model checking techniques are applied for the verification of predefined system properties, and thus, conducting a security analysis of a system. In this paper, we propose the utilization of automated and error-free model checking techniques for the verification of security properties in multi-domain AC systems. Therefore, we propose a formal definition in temporal logic of four AC system properties regarding secure inter-operation with Role-Based Access Control (RBAC) policies in order to be verified by using model checking. For this purpose, we demonstrate the implementation of a tool chain for expressing RBAC security policies, reasoning on role hierarchies and properly feeding the model checking process. The proposed approach can be applied in any RBAC model to efficiently detect non-conformance between an AC system and its security specifications. As a proof of concept, we provide examples illustrating the verification of the defined secure inter-operation properties in multi-domain RBAC policies.

The article shows the relevance of the widespread use of computer vision technology on the example of face recognition as part of the access control and management system. The main methods that are used in the implementation of classical control systems and access control are considered. The scheme for the implementation of the access control with face recognition technology is described. The use of this technology makes it possible to increase the level of information security of enterprises and, as a result, reduce the possible financial damage from the implementation of attacks on their assets from illegitimate penetration into the protected area through the access control system using the passes of legal users.

Face recognition is a kind of biometric identification technology based on the facial feature information of people. This article uses C51 single-chip microcomputer, OpenCV, etc. to complete the site access control system. By entering the face information, the system will open the access control if the system is recognized successfully, and will alarm if the recognition fails. This system completes the design requirements of the access control system and realizes the intelligent access control of the construction site.

Biometric authentication systems offer a secure and convenient method of verifying the identity of individuals based on their unique physiological or behavioral traits. This paper presents a comprehensive survey of biometric authentication systems, focusing on the principles, technologies, applications, challenges, and future directions in the field. The survey covers various biometric modalities, including fingerprint recognition, iris recognition, facial recognition, keystroke dynamics, voice recognition, and gait analysis. Key topics discussed include the use of multi-modal biometric systems, continuous authentication, machine learning, and AI in biometrics, and biometric encryption. The paper also explores the implementation of biometric authentication in access control systems, financial transactions, healthcare systems, and government and law enforcement. Challenges such as security and privacy concerns, spoofing and impersonation, scalability and integration, and ethical and legal issues are addressed. Future research opportunities include improving accuracy and reliability, enhancing security and privacy, addressing societal and ethical concerns, and integrating biometric authentication with the Internet of Things (IoT).

Towards more pro-active access control in computer systems and networks

Iris recognition, a relatively new biometric technology, has great advantages such as variability, stability and security, thus it is the most promising for high security environments. Among its applications are border control in airports and harbors, access control in laboratories and factories, identification for Automatic Teller Machines and restricted access to police evidence rooms. There have been several implementations of security systems using biometric, especially for identification and verification cases. The term biometrics is derived from the Greek words bio (life) and metric (to measure). The pattern used in the biometric is the iris pattern in human eye. The iris pattern has been proved unique for each person. A literature review of the most prominent algorithms implemented in each stage is presented. This paper provides a review of major iris I. Introduction Biometric recognition is an emerging technology which employs the physiological and behavioral characteristics to identify an individual. The physiological characteristics include the iris, fingerprint, face and hand geometry. Voice and signature are categorized as the behavioral characteristics. Among these, the human iris is an annular region between the sclera (the white portion of the eye) and the pupil (the darkest portion of the eye). Iris is gaining lots of attention due its unique pattern. The patterns that give uniqueness to the iris are the coronas, furrows, stripes and so on (1). These patterns thus, distinguish the individual as the genuine and the imposter, and make the iris recognition particularly the promising solution. Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high resolution images of the irises of an individual's eyes (16). The main stages of an iris recognition system are: image pre-processing which consists of iris localization, iris normalization; iris feature extraction and template matching (13)(15). It is necessary to obtain the iris region to carry out the feature extraction and the matching. Eyelids and the eyelashes that may cover the iris region are detected and removed. The normalization step is the conversion of Cartesian co-ordinates to the polar co-ordinates. Various image enhancement algorithms can also applied in order to compensate the non uniformity and low contrast characters in the iris portion. Feature extraction is the process of extracting texture from the region of interest and then using these features as parameters for comparing two iris templates. The significant features of the iris are obtained for accurate identification purpose. Template matching compares the user template with the templates from the dataset or the trained dataset using a matching metric. The matching metric will give the measure of similarity between two iris templates. This paper discuses the algorithmic implementation in each stage. Iris image capture stage will not be discussed in this paper. The rest of the stages are discussed next sections including image preprocessing, iris feature extraction and template matching.

This paper first brief reviews the state of the security technology research and access control in the web service environment. Despite recent advances in access control approaches for Web Services, the heterogeneity of subjects and objects in Web Service environment has made it difficult to development of effective access control system. So we present a context-aware service-orient role-based access control model (CSRBAC). In CSRBAC model, access control system can make its access control decisions by capturing security relevant environmental context, such as time, location, operation state, or other environmental information. Based on CSRBAC model, a secure architecture model for Web Services is presented. It implements an access control system with dynamically grant and adapt permissions to users based on their current context. Compared to traditional access control mechanisms, the CSRBAC model can provide management flexibility and improved security for Web Services applications.KeywordsAccess ControlRole AssignmentContext ParameterPolicy Decision PointAccess Control SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In today's society, the rapid development of the Internet makes People's Daily life become more intelligent and diversified. Today's society has entered a multifaceted era where everything is interconnected. Artificial intelligence technology is gradually replacing some traditional human services, such as intelligent robot customer service instead of traditional human customer service, intelligent face scanning security check in railway stations instead of traditional manual ticket checking, unmanned supermarket automatic checkout has liberated some social labor costs. All these changes are the result of the development of artificial intelligence technology in today's society. In recent years, unicorn startups focused on biometrics have sprung up all around us, such as BTU and its MEG VII (Face ++). Thanks to the development of Internet and artificial intelligence technology, in many application fields, the traditional access control and identity authentication technology based on password verification is gradually transforming to the scheme based on biometric identification verification. Secure identity authentication is very important to the application of Internet. Face recognition is the most popular technology among all biometric identification technologies. In the field of biometric identification technology, it has become the most widely used technology in the field of identity authentication because of its unique non-invasive, support for infrared and visible light, no need for user cooperation and many other advantages. In the field of education, examinee identification, pedestrian identification detection at the entrance of railway stations, face electronic payment, intelligent video surveillance system, intelligent attendance and access control system, intelligent unmanned supermarkets and customs clearance ports become the pioneer fields of face recognition applications. It can be seen that the era of “national face brushing” has arrived, and the application of face recognition technology will only be more and more widespread in the current era and in the future. However, due to the sensitivity of biometric data and the heterogeneity and openness of network environment, the privacy leakage of biometric data is difficult to avoid. At present, fog computing and edge computing have been paid more and more attention in many fields. In the case that cloud service providers are unable to provide sufficient security, edge computing shows its advantages. In this paper, mobile edge computing is introduced for the first time into the face privacy protection identity authentication system based on cloud server outsourcing computing. It can not only greatly reduce the interaction frequency between users and cloud server, improve the availability and fault tolerance of the system, but also contribute to the implementation of privacy protection scheme. A deep constitutional neural network for face feature extraction is trained using deep learning framework Cafe. Cosine similarity is used to complete face verification. A privacy protection scheme based on the secure nearest neighbor algorithm is proposed, which can not only protect the security of the face feature data at the edge computing node, but also allow the edge computing node to complete the face recognition operation against the encrypted face feature data. In addition, the encryption scheme does not require large computing resources, and the accuracy of face recognition in cipher text is exactly the same as that in explain. At present, most of the solutions either have high computational complexity or poor security performance. How to reduce the computational complexity and improve the real-time performance of the system while ensuring the high security of the private data has important research significance and value. Therefore, in the cloud server outsourcing computing environment, how to complete biometric identification on the premise of protecting the privacy of biological data has become a research hot spot.

Access control and security within higher education institutions are of paramount importance in safeguarding sensitive information and resources. Conventional authentication methods, such as passwords and identification cards, have proven susceptible to security breaches and identity theft. To address these vulnerabilities, this paper presents a novel biometric authentication scheme tailored to the unique needs of higher education institutions. Deploying a trustworthy user authentication system became a key responsibility for both access control and securing user's private data with the rapid rise of electronic crimes and their connected difficulties. For both private and public use, human biometric features including voice, finger, iris scanning, face, signature, and other features offer a solid security level. This paper provides a comprehensive overview of the biometric authentication scheme, outlining its architecture, functionality, and security measures. We also present the results of a pilot implementation within a higher education institution, demonstrating improved access security and user satisfaction. Ethical considerations and privacy safeguards are discussed to ensure responsible biometric data handling. For a long time, numerous biometric authentication solutions have been considered. owing to the distinctiveness of human biometrics, which was important in thwarting imposters' attacks. Only a few of the key issues endangering system integrity and impeding effective service delivery include identity theft, spoofing, and the reliability of authentication systems in higher education institutions. From the experiment the total number of tests was 15, as the threshold was one attempt. While fingerprint authentication typically took 2.67 seconds, palm vein authentication often took 9.15 seconds. Therefore, the palm vein was slower than the fingerprint in terms of speed. The structure of the hand and the distance between the palm and the scanner were the determining elements in pal's slow authentication speed. The palm vein system has a 93.33% accuracy rate compared to the fingerprint system's 60% accuracy rate, making it the preferable model to use in a higher education setting. A biometric system's success or failure is influenced by a variety of variables and application domains. The purpose of this work is to discuss an appropriate biometric authentication model that may be used to improve the reliability of biometric systems in institutions of higher learning. The proposed biometric authentication scheme offers a forward-looking solution to the access integrity challenges faced by higher education institutions. By adopting this technology, institutions can bolster their security posture, protect sensitive data, and provide a more convenient and secure access experience for students, faculty, and staff.

In the modern digital world, biometric systems are a significant part of personal identification and access control. The use of sensitive biometric data such as iris and facial features raises huge privacy concerns because of the possibilities of data breaches and misuse. Therefore, this project focuses on the enhancement of the security of multimodal biometric systems through encryption mechanisms. The idea is to generate a strong biometric key based on features produced from iris and face biometric systems through advanced machine learning techniques. The features are then employed to produce a strong biometric key capable of encrypting the secret value using the AES algorithm. AES is a widely adopted symmetric encryption algorithm that ensures high efficiency in its software implementation and seamless processing capabilities of image data. This provides not only the reinforcement of authentication but also maintains confidentiality of biometric characteristics and mitigates the risks of privacy attacks and unauthorized access. Thus, this multimodal biometric encryption improves reliability, robustness, and the resistance power of the system against potential attacks, making it a promising step forward in the realm of secure biometric authenticationsystems. Keywords- Biometric encryption,Multimodal biometrics, ,Privacy attacks,Advanced Encryption Standard (AES), Biometric key generation,Iris recognition,Face recognition,Machine learning,Bio-crypto systems, Data security.

Face recognition technology has become more and more important in the field of biometric identification because of its advantages. A face recognition system combining hardware and software based on S3C2440 is presented in this paper. We adopt S3C2440 embedded development board as the hardware platform. This paper creates an embedded Linux software platform and our system is divided into four sections by their function: graphical user interface, image capture, face detection and face recognition. The whole system is tested as an access control system in practical situation. Experimental results show that our system has a good performance in both accuracy and efficiency of face recognition in access control.

A dormitory is a place where students live and live for a long time, and the safety of the dormitory can directly affect the quality of students’ studies and lives. In light of this, the study constructs a dynamic security planning system for college dormitory areas, enhancing their security and intelligence through the application of intelligent technology and multi-directional management coordination. Taking the access control system and fire monitoring system in the dormitory security prevention system as an example, based on the neural network perspective, we design the face recognition method of HOG-YOLO and the fire detection algorithm of improved YOLOv5 to realize, train, and test the two models, and evaluate the face recognition performance and fire target detection effect of the models. The results show that the face recognition method established in this paper is highly accurate and rapid, with a recognition accuracy of 95.7%, and it can complete recognition and detection under different facial angles. The fire detection algorithm’s FPS has been improved by 14.75%. The overall accuracy has improved by more than 1%, which has a good effect on fire monitoring. Planning the security system for the dormitory areas in higher education is a significant area of research for ensuring campus security, and access control and fire monitoring systems have significant application potential.

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.