When Face Recognition Goes Wrong
Autor Catriona Havarden Limba Engleză Paperback – 29 oct 2024
Concluding with a look to the future of facial recognition, the author asks the world’s leading experts what are the big questions that still need to be answered, and can we train humans and machines to be super recognisers? This book is a must-read for anyone interested in facial recognition, or in psychology, criminal justice and law.
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Specificații
ISBN-13: 9781032010953
ISBN-10: 1032010959
Pagini: 274
Ilustrații: 44
Dimensiuni: 129 x 198 x 18 mm
Greutate: 0.27 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
ISBN-10: 1032010959
Pagini: 274
Ilustrații: 44
Dimensiuni: 129 x 198 x 18 mm
Greutate: 0.27 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom
Public țintă
General, Postgraduate, and Undergraduate AdvancedCuprins
Chapter 1. Introduction to face recognition and eyewitness identification. Chapter 2. I recognise your face but who are you? Everyday face recognition errors. Chapter 3. Unconscious transference and misidentifications. Chapter 4. The influence of race on face recognition and eyewitness identification. Chapter 5. The influence of gender on face recognition and eyewitness identification. Chapter 6. The influence of age upon face recognition and eyewitness identification. Chapter 7. Machine errors in face recognition. Chapter 8. Disguises, face masks and appearance change. Chapter 9. Prosopagnosia, Face Blindness. Chapter 10. Delusions of Misidentification (DMI). Chapter 11. Super recognisers. Chapter 12. The future of face recognition research.
Notă biografică
Catriona Havard is Professor of Psychology in the School of Psychology & Counselling at The Open University, UK.
Descriere
This book explores the myriad ways that humans and machines make mistakes in facial recognition. Adopting a critical stance throughout, the book explores why and how humans and machines make mistakes, covering topics including racial and gender biases, neuropsychological disorders, and widespread algorithm problems.