enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Eigenface - Wikipedia

    en.wikipedia.org/wiki/Eigenface

    Matlab example code for eigenfaces; OpenCV + C++Builder6 implementation of PCA; Java applet demonstration of eigenfaces Archived 2011-11-01 at the Wayback Machine; Introduction to eigenfaces; Face Recognition Function in OpenCV; Eigenface-based Facial Expression Recognition in Matlab

  3. Active appearance model - Wikipedia

    en.wikipedia.org/wiki/Active_appearance_model

    The model was first introduced by Edwards, Cootes and Taylor in the context of face analysis at the 3rd International Conference on Face and Gesture Recognition, 1998. [1] Cootes, Edwards and Taylor further described the approach as a general method in computer vision at the European Conference on Computer Vision in the same year.

  4. FaceNet - Wikipedia

    en.wikipedia.org/wiki/FaceNet

    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]

  5. Viola–Jones object detection framework - Wikipedia

    en.wikipedia.org/wiki/Viola–Jones_object...

    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.

  6. Face detection - Wikipedia

    en.wikipedia.org/wiki/Face_detection

    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. [2]

  7. Landmark detection - Wikipedia

    en.wikipedia.org/wiki/Landmark_detection

    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]

  8. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    Some projects use adversarial machine learning to come up with new printed patterns that confuse existing face recognition software. [ 246 ] One method that may work to protect from facial recognition systems are specific haircuts and make-up patterns that prevent the used algorithms to detect a face, known as computer vision dazzle . [ 238 ]

  9. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    AlexNet architecture and a possible modification. On the top is half of the original AlexNet (which is split into two halves, one per GPU). On the bottom is the same architecture but with the last "projection" layer replaced by another one that projects to fewer outputs.