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Face Recognition is used to identify or verify a person from a digital image or a video source using a pre-stored facial data. Visage SDK's face recognition algorithms can measure similarities between people and recognize a person’s identity [citation needed] from a frontal facial image by comparing it to pre-stored faces.
This is a stand-alone camera that can be attached to a desktop or laptop computer. [21] It is intended to be used for natural gesture-based interaction, face recognition, immersive, video conferencing and collaboration, gaming and learning and 3D scanning. [22] There was also version of this camera to be embedded into laptop computers. [18]
Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age.
The face recognition system was initially installed as part of a US$11 million contract and included a computer cluster of sixty computers, a fiber-optic cable network for the airport buildings, as well as the installation of 150 surveillance cameras in the airport terminal and at about 30 airport gates.
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1]
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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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