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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.
DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.
In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face detection simply answers two question, 1. are there any human faces in the collected images or video? 2. where is the face located?
To search for the object in the entire frame, the search window can be moved across the image and check every location with the classifier. This process is most commonly used in image processing for object detection and tracking, primarily facial detection and recognition. The first cascading classifier was the face detector of Viola and Jones ...
Learning-based fitting methods use machine learning techniques to predict the facial coefficients. These can use linear regression, nonlinear regression and other fitting methods. [6] In general, the analytic fitting methods are more accurate and do not need training, while the learning-based fitting methods are faster, but need to be trained. [7]
Some solutions proposed include attention mechanisms, few-shot learning, disentanglement, boundary conversions, and skip connections. Occlusions. When part of the face is obstructed with a hand, hair, glasses, or any other item then artifacts can occur. A common occlusion is a closed mouth which hides the inside of the mouth and the teeth.
BGSLibrary includes the original LBP implementation for motion detection [12] as well as a new LBP operator variant combined with Markov Random Fields [13] with improved recognition rates and robustness. dlib, an open source C++ library: implementation. scikit-image, an open source Python library. Provides a c-based python implementation for LBP
Feature detection includes methods for computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.