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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]
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?
The position of these rectangles is defined relative to a detection window that acts like a bounding box to the target object (the face in this case). In the detection phase of the Viola–Jones object detection framework, a window of the target size is moved over the input image, and for each subsection of the image the Haar-like feature is ...
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.
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]
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research . These datasets consist primarily of images or videos for tasks such as object detection , facial recognition , and multi-label classification .
Face hallucination algorithms that are applied to images prior to those images being submitted to the facial recognition system use example-based machine learning with pixel substitution or nearest neighbour distribution indexes that may also incorporate demographic and age related facial characteristics. Use of face hallucination techniques ...
Celebrity recognition in images [3] [4]; Facial attribute detection in images, including gender, age range, emotions (e.g. happy, calm, disgusted), whether the face has a beard or mustache, whether the face has eyeglasses or sunglasses, whether the eyes are open, whether the mouth is open, whether the person is smiling, and the location of several markers such as the pupils and jaw line.