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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'.
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.
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.
Cascade classifiers are available in OpenCV, with pre-trained cascades for frontal faces and upper body. Training a new cascade in OpenCV is also possible with either haar_training or train_cascades methods. This can be used for rapid object detection of more specific targets, including non-human objects with Haar-like features. The process ...
In computer vision, the Kanade–Lucas–Tomasi (KLT) feature tracker is an approach to feature extraction.It is proposed mainly for the purpose of dealing with the problem that traditional image registration techniques are generally costly.
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]
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The feature vector can then be further processed for many different tasks. For example, to identify the face, one can compare it against a list of feature vectors of known faces, and identify the face with the most similar feature vector. DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces.