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Future Trends in Condition Assessment
Published in Justin Starr, Water and Wastewater Pipeline Assessment Technologies, 2021
While AI is commonly associated with robots and science fiction, some of the core technologies are relatively simple to understand and have already been implemented in everyday objects. For example, AI based on computer vision and image processing is used by iPhones as part of the “Face ID” function, where the device will be unlocked when an authorized user’s face is visible in the camera. This isn’t just pattern matching – the system understands what makes a user’s face significant – even when obscured by facial hair or glasses, or when presented in different lighting situations. AI enables the system to develop a more detailed picture of what the user looks like by processing more and more data over time (Figure 11.4).
Introduction
Published in Hassan Ugail, Deep Learning in Visual Computing, 2022
In September 2017, Apple released its iPhone X. With it came the Face ID—Apple’s advanced facial recognition-based biometric system-embedded within the hardware and the software of the phone. Shortly, I managed to lay my hands on an iPhone X. To use facial recognition as an identification system—and to gain access to various Apps, including the App that lets me into my bank account—I first had to enroll my face. The enrolling process involved following a simple set of instructions where I had to present my face at certain poses and angles to the phone’s camera. Within a few minutes of enrolling my face, I was able to start enjoying the convenience of simply looking at my phone to unlock it and use it.
Rapid Parallel Search Technology with Scanning Electron Microscope and Artificial Neural Network
Published in Smart Science, 2023
‘Face ID’ system for facial recognition designed and developed by ‘Apple Inc.’ for the iPhone allows biometric authentication to unlock the device. This technology is based on a sensor that consists of a dot projector module that projects more than 30,000 infrared dots onto the user’s face. ‘Apple Inc.’ claimed the probability of someone else unlocking an iPhone with ‘Face ID’ is 1 in 1 million. It is logical that for the tiniest sample described in 3D MAD consists of 22,500 points the probability decreased by ~3/2 times. On account of using a grayscale 8-bit dataset, instead of a color RGB 24-bit one, this probability is further reduced by three times.
An LDOP approach for face identification under unconstrained scenarios
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Rinku Datta Rakshit, Ajita Rattani, Dakshina Ranjan Kisku
Using digital images or video frames from a video source, a facial recognition system can verify or identify a person (Jain & Li, 2011). While the input face image is compared to the template of the claimed identity in the verification mode, the input face image is matched to the templates of all the individuals who have been enrolled in the dataset for identity inference in the identification mode. Face recognition is a technique that is widely used in surveillance, border control, healthcare, banking services and, most recently, mobile user identification with Apple’s ‘Face ID’ name with iPhone X (Hirayama et al., 2019).
Reversing the spectator paradigm: symbiotic interaction and the ‘gaze’ of the artwork
Published in Digital Creativity, 2019
In September 2017 Apple presented a new model of the iPhone with a new identification standard called FaceID—basically the phone contains a device for scanning faces. As a result, it is possible to use this face recognition device to unlock the phone and sign documents. This was visualized in commercial ad for phone users by red rays emerging from the camera of the phone and hitting the face surface at thousands of points which document the physical relief of the face. This information is recorded in a specific location of the device which is accessible only to the user.