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Real-time face detection in video footage became possible in 2001 with the Viola–Jones object detection framework for faces. [28] Paul Viola and Michael Jones combined their face detection method with the Haar-like feature approach to object recognition in digital images to launch AdaBoost, the first real-time frontal-view face detector. [29]
The fusiform face area (FFA, meaning spindle-shaped face area) is a part of the human visual system (while also activated in people blind from birth [1]) that is specialized for facial recognition. [2] It is located in the inferior temporal cortex (IT), in the fusiform gyrus (Brodmann area 37).
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.
Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition .
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.
Any human face can be considered to be a combination of these standard faces. For example, one's face might be composed of the average face plus 10% from eigenface 1, 55% from eigenface 2, and even −3% from eigenface 3. Remarkably, it does not take many eigenfaces combined together to achieve a fair approximation of most faces.
DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces. The facial alignment begins with a 2D alignment, and then continues with 3D alignment and frontalization. That is, DeepFace's process is two steps. First, it corrects the angles of an image so that the face in the photo is looking forward.
Holistic methods are pre-programmed with statistical information on face shape and landmark location coefficients. The classic holistic method is the active appearance model (AAM) introduced in 1998. [3] Since then there has been a number of extensions and improvements to the method.
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