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Automatic face detection with OpenCV. 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.
Facial recognition software at a US airport Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system [1] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
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.
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel [3]).
OpenCV provides a comprehensive set of functions that can support real-time computer vision applications, such as image recognition, motion tracking, and facial detection. [68] Originally developed by Intel, OpenCV has become one of the most popular libraries for computer vision due to its versatility and extensive community support.
A library to allow development of marker-based, Natural Feature Tracking and location-based AR applications on the web. It can be used in conjunction with A-Frame (virtual reality framework) or three.js: MindAR: 2021 [19] MIT: A library to allow development of image-tracking and face-tracking types of AR applications on the web.
ARToolKit is an open-source computer tracking library for creation of strong augmented reality applications that overlay virtual imagery on the real world. Currently, it is maintained as an open-source project hosted on GitHub. [2]