Face Recognition Attendance System

Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There’s also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty. Keywords: Smart Attendance System, NFC, RFID, OpenCV, NumPy

Travellers' Intentions to Use Facial Recognition Systems for Authentication in Hotels

With the rapid development of this technology, facial recognition systems have become widely adopted in recent years. The application of the facial recognition systems by the hotel industry has resulted in a novel service model, as well as in high expectations. These systems can be used to improve conventional services and can also enhance hotel security. Based on theory, this paper employs a technology acceptance model to gain a deeper understanding of how travelers' intention to use facial recognition systems for authentication is formed. This paper employed the survey method and used data from 413 subjects to develop a model yielding results with both theoretical and management implications. These results highlight the advantages and potential commercial value of facial recognition systems, and can provide useful analysis and suggestions for the hotel industry.

Visual Gesture-Based Character Recognition Systems for Design of Assistive Technologies for People With Special Necessities

In the present scenario, around 15% of the world's population experience some form of disability. So, there has been an enormous increase in the demand for assistive techniques for overcoming the restraints faced by people with physical impairments. More recently, gesture-based character recognition (GBCR) has emerged as an assistive tool of immense importance, especially for facilitating the needs of persons with special necessities. Such GBCR systems serve as a powerful mediator for communication among people having hearing and speech impairments. They can also serve as a rehabilitative aid for people with motor disabilities who cannot write with pen on paper, or face difficulty in using common human-machine interactive (HMI) devices. This chapter provides a glimpse of disability prevalence around the globe and particularly in India, emphasizes the importance of learning-based GBCR systems in practical education of differently-abled children, and highlights the novel research contributions made in this field.

A Meta-analysis of Privacy, Ethical and Security Aspects of Facial Recognition Systems

Facial recognition systems use advanced computing to capture facial information and compare the same with proprietary databases for validation. The emergence of data capturing intermediaries and open access image repositories have compounded the need for a holistic perspective for handling the privacy and security challenges associated with FRS. The study presents the results of a bibliometric analysis conducted on the topic of privacy, ethical and security aspects of FRS. This study presents the level of academic discussion on the topic using bibliometric performance analysis. The results of the bibliographic coupling analysis to identify the research hotspots are also presented. The results also include the systematic literature review of 148 publications that are distributed across seven themes. Both the bibliometric and systematic analysis showed that privacy and security in FRS requires a holistic perspective that cuts across privacy, ethical, security, legal, policy and technological aspects.

Digital Technology in Diagnostic Breast Pathology and Immunohistochemistry

Digital technology has been used in the field of diagnostic breast pathology and immunohistochemistry (IHC) for decades. Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition systems, and digital technology-based production of antibodies and other IHC reagents. However, the recent application of whole slide imaging technology and artificial intelligence (AI)-based tools has attracted a lot of attention. The use of AI tools in breast pathology is discussed briefly as it is covered in other reviews. Here, we present the main application of digital technology in IHC. This includes automation of IHC staining, using image analysis systems and computer vision technology to interpret IHC staining, and the use of AI-based tools to predict marker expression from haematoxylin and eosin-stained digitalized images.

Efficient Thinning Algorithm for Malaysian Car Plate Character Recognition

The thinning algorithm is one of the approaches of identifying each character printed on the car plate. Malaysian car plate characters appear in different character sizes, styles, customized printed characters etc. These variations contribute to difficulty in thinning successfully segmented and extracted license plate characters for recognition. To address these problems, an improved thinning operation for Malaysian car plate character recognition is proposed. In this algorithm, samples from segmented and extracted license plates are used for a thinning operation which is passed to Zhang-Suen thinning algorithm that could not guarantee one pixel thick and then to single pixelate algorithm that provides one pixel width of character for recognition. From the simulation, the result obtained has clearly proven to be the best for character recognition systems with least number of white pixels (777 pixels) and 0.26% redundant pixel left in the medial curve.