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The Viola–Jones algorithm for face detection uses Haar-like features to locate faces in an image. Here a Haar feature that looks similar to the bridge of the nose is applied onto the face. Until the 1990s, facial recognition systems were developed primarily by using photographic portraits of human faces.
DeepFace is a deep learning facial recognition system created by a research group at Facebook.It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. [1] Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.
For a discussion on the vulnerabilities of Facenet-based face recognition algorithms in applications to the Deepfake videos: Pavel Korshunov; Sébastien Marcel (2022). "The Threat of Deepfakes to Computer and Human Visions" in: Handbook of Digital Face Manipulation and Detection From DeepFakes to Morphing Attacks (PDF). Springer. pp. 97– 114.
FindFace is a face recognition technology developed by the Russian company NtechLab that specializes in neural network tools. The company provides a line of services for the state and various business sectors based on FindFace algorithm.
Facial recognition or face recognition may refer to: Face detection, often a step done before facial recognition; Face perception, the process by which the human brain understands and interprets the face; Pareidolia, which involves, in part, seeing images of faces in clouds and other scenes
Face ID is a biometric authentication facial recognition system designed and developed by Apple Inc. for the iPhone and iPad Pro.The system can be used for unlocking a device, [1] making payments, accessing sensitive data, providing detailed facial expression tracking for Animoji, as well as six degrees of freedom (6DOF) head-tracking, eye-tracking, and other features.
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
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