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    Facial Recognition System: Fundamentals and Applications
    Facial Recognition System: Fundamentals and Applications
    Facial Recognition System: Fundamentals and Applications
    Ebook98 pages1 hourArtificial Intelligence

    Facial Recognition System: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Facial Recognition System


    A facial recognition system is a technology that is able to match a human face from a digital image or a video frame against a database of faces. The human face can be captured in either still or moving images. This type of technology is generally used to verify customers through ID verification services. It accomplishes this task by locating and measuring face features from within an image that has been provided to it.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Facial recognition system


    Chapter 2: Biometrics


    Chapter 3: Face detection


    Chapter 4: Biometric points


    Chapter 5: Identity-based security


    Chapter 6: DeepFace


    Chapter 7: Visage SDK


    Chapter 8: Algorithmic bias


    Chapter 9: Amazon Rekognition


    Chapter 10: Clearview AI


    (II) Answering the public top questions about facial recognition system.


    (III) Real world examples for the usage of facial recognition system in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of facial recognition system' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of facial recognition system.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 6, 2023
    Facial Recognition System: Fundamentals and Applications

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      Book preview

      Facial Recognition System - Fouad Sabry

      Chapter 1: Facial recognition system

      A facial recognition system is a technology that is capable of matching a human face from a digital image or a video frame against a database of faces. These systems are typically used to authenticate users through ID verification services. Facial recognition systems work by locating and measuring facial features from a given image.

      In the 1960s, comparable systems started to be developed, first as a sort of computer application. Since its introduction, face recognition systems have found increased use in recent years, particularly on smartphones as well as in other kinds of technology, such as robots. Facial recognition software falls under the category of biometrics since it relies on the analysis of a person's physiological features in order to identify them. Even though the accuracy of face recognition systems as a biometric technology is lower than that of iris recognition and fingerprint recognition, it has gained widespread adoption owing to the fact that the procedure does not need physical touch. This modification will be one of the most significant revolutions in the use of face recognition technology in the annals of that field's history.

      The 1960s saw the birth of the first automated face recognition systems. Woody Bledsoe, Helen Chan Wolf, and Charles Bisson collaborated on developing software that would enable a computer to identify human faces. The early iteration of their face recognition project was referred to as the man-machine system. This was due to the fact that the coordinates of the facial characteristics in an image needed to be defined by a person before the computer could utilize them for recognition. A human being had to use a graphics tablet to precisely locate the coordinates of several face characteristics, such as the pupil centers, the inner and outside corner of the eyes, and the widow's peak in the hairline. Using the coordinates, we were able to determine a total of 20 distances, including the breadth of the mouth as well as the distance between the eyes. In this approach, a human being might analyze around 40 images in one hour and, as a result, develop a database including the determined distances. The distances between each image would then be automatically compared by a computer, and the difference in those distances would be calculated. The computer would then provide the closed records as a probable match.

      Before the 1990s, the development of facial recognition systems was predominantly accomplished via the use of photographic portraits of human faces. Research on face recognition to accurately detect a face in an image that also includes other objects began to gain pace in the early 1990s with the use of the principle component analysis (PCA). Matthew Turk and Alex Pentland are responsible for the development of the PCA technique of face detection, which is also known as the Eigenface approach.

      Clearview AI gave the software to the Ukrainian government as a donation. It is believed that Russia is making use of it to locate anti-war protestors. Initially developed for use by police enforcement in the United States The use of it in war dead gives rise to additional worries. Stephen Hare, a surveillance specialist based in London, is concerned that it may give the impression that the Ukrainians are inhuman: Is it really having the desired effect? Or does it cause Russians to say things like, Look at those lawless Ukrainians being harsh to our lads, as a result?

      While it doesn't take much effort for people to identify one another's faces, The identification of a subject's facial characteristics by some face recognition algorithms involves the extraction of landmarks or features from a picture of the subject's face. An algorithm may, for instance, evaluate the location, size, and/or form of the jaw in relation to the eyes, nose, cheekbones, and other facial features. applied to a select group of prominent facial characteristics, resulting in a portrayal of the face that is somewhat condensed.

      There are two primary methods that can be used to develop recognition algorithms: the geometric method, which concentrates on distinguishing characteristics, and the photo-metric method, which is a statistical method that reduces an image to a set of values and then compares those values to templates in order to eliminate variations. Some people divide these algorithms into two primary groups: holistic and feature-based models. [Citation needed] [Citation needed] The first method seeks to identify the face in its whole, but the second method, which is feature-based, breaks the face down into its component parts, such as according to characteristics, and analyzes each part together with its spatial placement in relation to the other parts.

      To facilitate human identification at a distance (HID) low-resolution photographs of faces are augmented via face hallucination. In CCTV images, people's faces are often quite hard to make out. However, because facial recognition algorithms that identify and plot facial features require images with a high resolution, resolution enhancement techniques have been developed to enable facial recognition systems to work with imagery that has been captured in environments with a high signal-to-noise ratio. This is possible because resolution enhancement techniques allow facial recognition systems to work with imagery that has been captured in environments with a high signal-to-noise ratio. Face hallucination algorithms are applied to images prior to those images being submitted to the facial recognition system. These algorithms use example-based machine learning in conjunction with pixel substitution or nearest neighbour distribution indexes, and they may also incorporate demographic and age-related facial characteristics. Use of face hallucination methods increases the effectiveness of high resolution facial

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