Reading Faces
Facial Biometrics from Aristotle to Artificial Intelligence
Seiten
2025
Routledge (Verlag)
978-1-032-82684-4 (ISBN)
Routledge (Verlag)
978-1-032-82684-4 (ISBN)
Reading Faces is the first book to discuss the historical contexts and theoretical underpinnings of the artificial intelligence systems (AIs) that identify individuals, or purport to ascribe emotions and characteristics to them based on their facial features.
Reading Faces is the first book to discuss the historical contexts and theoretical underpinnings of the artificial intelligence (AI) systems that identify individuals, or purport to ascribe emotions and characteristics to them based on their facial features.
It examines the direct impact of past research on the development of AI systems; the way in which the past provides contexts which have predisposed people to accept outputs of AI systems as ‘common sense’; how the role of politics and the changing nature of the state have led to the introduction of AI systems and their precursors; how previous technologies led to the acceptance of the introduction of superficially similar AIs; and how the outputs – and biases, such as purported associations between race and criminality – of facial biometrics systems have been determined by the historical nature of the underlying datasets they analyze. Reading Faces approaches these issues within the sweep of Western History – how it has shaped conceptual categories about the application and ethics of AI, and tried to foist those ideas and values on the Global South.
This book is essential reading for postgraduates, scholars, as well as non-specialist readers, such as booksellers and librarians, interested in modern AI technology.
Reading Faces is the first book to discuss the historical contexts and theoretical underpinnings of the artificial intelligence (AI) systems that identify individuals, or purport to ascribe emotions and characteristics to them based on their facial features.
It examines the direct impact of past research on the development of AI systems; the way in which the past provides contexts which have predisposed people to accept outputs of AI systems as ‘common sense’; how the role of politics and the changing nature of the state have led to the introduction of AI systems and their precursors; how previous technologies led to the acceptance of the introduction of superficially similar AIs; and how the outputs – and biases, such as purported associations between race and criminality – of facial biometrics systems have been determined by the historical nature of the underlying datasets they analyze. Reading Faces approaches these issues within the sweep of Western History – how it has shaped conceptual categories about the application and ethics of AI, and tried to foist those ideas and values on the Global South.
This book is essential reading for postgraduates, scholars, as well as non-specialist readers, such as booksellers and librarians, interested in modern AI technology.
Edward Higgs is Professor Emeritus at the School of Philosophical, Historical, and Interdisciplinary Studies (PHAIS) of the University of Essex. Among his other publications are The Information State in England: The Central Collection of Information on Citizens since 1500 (2004) and Identifying the English: Personal Identification 1500 to the Present (2011).
1. Introduction 2. Facial Recognition 3. Physiognomy and Character 4. Expressions and Emotion Detection 5. Criminality and the Face of Evil 6. The Face of Race 7. Some Overall Conclusions
| Erscheinungsdatum | 12.09.2025 |
|---|---|
| Zusatzinfo | 19 Halftones, black and white; 19 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 460 g |
| Themenwelt | Geschichte ► Allgemeine Geschichte ► Zeitgeschichte |
| Geschichte ► Teilgebiete der Geschichte ► Kulturgeschichte | |
| Naturwissenschaften | |
| ISBN-10 | 1-032-82684-3 / 1032826843 |
| ISBN-13 | 978-1-032-82684-4 / 9781032826844 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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