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  2. FaceNet - Wikipedia

    en.wikipedia.org/wiki/FaceNet

    The system uses the triplet loss function as its cost function and introduced a new online triplet mining method. The system achieved an accuracy of 99.63%, which is the highest score to date on the Labeled Faces in the Wild dataset using the unrestricted with labeled outside data protocol.

  3. Face Recognition Vendor Test - Wikipedia

    en.wikipedia.org/wiki/Face_Recognition_Vendor_Test

    FRVT Ongoing now has roughly 200 face recognition algorithms and tests against at least six collections of photographs [5] with multiple photographs of more than 8 million people. The best algorithms for 1:1 verification gives False Non Match Rates of 0.0003 at False Match Rates of 0.0001 on high quality visa images. [6] Additional programs:

  4. Speeded up robust features - Wikipedia

    en.wikipedia.org/wiki/Speeded_up_robust_features

    Accordingly, the scale space is analyzed by up-scaling the filter size rather than iteratively reducing the image size. The output of the above 9×9 filter is considered as the initial scale layer at scale s =1.2 (corresponding to Gaussian derivatives with σ = 1.2).

  5. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    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.

  6. Scale-invariant feature transform - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalised Hough transform.

  7. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2).

  8. Template matching - Wikipedia

    en.wikipedia.org/wiki/Template_matching

    Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [ 2 ] navigation of mobile robots , [ 3 ] or edge detection in images.

  9. Comparison of image viewers - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_image_viewers

    Yes Catalog-sets, date, filetypes, events, author, people, by search, by keywords and all metadata Yes Yes name, date, size, user-defined Picasa: Partial only in slideshow mode Yes Partial no view window, fit to viewing area, 1:1, zoom to 400% No Yes user defined Yes database directory-tree, albums, timeline Yes Partial name, date, size, user ...