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3D model of a human face. Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition.
The algorithms for solving this problem are specialized for locating a single pre-identified object, and can be contrasted with algorithms which operate on general classes of objects, such as face recognition systems or 3D generic object recognition. Due to the low cost and ease of acquiring photographs, a significant amount of research has ...
Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. [36] These features are then used to search for other images with matching features. [37]
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]
Historically, face models have been the first example of morphable models, and the field of 3DFMM remains a very active field of research as today. In fact, 3DFMM has been successfully employed in face recognition , [ 8 ] entertainment industry ( gaming and extended reality , [ 9 ] [ 10 ] virtual try on , [ 11 ] face replacement , [ 12 ] face ...
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.
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? Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit ...
3D object recognition, [30] gesture recognition and hand tracking [31] Problem Statement: Mostly algorithms available for 3D reconstruction are extremely slow and cannot be used in real-time. Though the algorithms presented are still in infancy but they have the potential for fast computation. Existing Approaches: Delaunay triangulation (25 Points)