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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.
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.
The technique used in creating eigenfaces and using them for recognition is also used outside of face recognition: handwriting recognition, lip reading, voice recognition, sign language/hand gestures interpretation and medical imaging analysis. Therefore, some do not use the term eigenface, but prefer to use 'eigenimage'.
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]
The face recognition system is deployed to identify individuals among the travellers that are sought by the Panamanian National Police or Interpol. [140] Tocumen International Airport operates an airport-wide surveillance system using hundreds of live face recognition cameras to identify wanted individuals passing through the airport.
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.
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]
OpenCV's Cascade Classifiers support LBPs as of version 2. VLFeat , an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation . LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem.