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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 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.
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]
Starting with version 2.0, digiKam has introduced face recognition allowing you to automatically identify photos of certain people and tag them. DigiKam's photo manager was the first free project to feature similar functionality, with face recognition previously implemented only in proprietary products such as Google Picasa, Apple's Photos, and Windows Live Photo Gallery.
The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009.
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.
Gender classification, face detection, face recognition, age estimation, and glasses detection 2017 [119] [120] Afifi, M. et al. IMDb-WIKI IMDb and Wikipedia face images with gender and age labels. None 523,051 Images Gender classification, face detection, face recognition, age estimation 2015 [121] R. Rothe, R. Timofte, L. V. Gool