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  2. 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:

  3. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    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]

  4. Face detection - Wikipedia

    en.wikipedia.org/wiki/Face_detection

    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.

  5. PimEyes - Wikipedia

    en.wikipedia.org/wiki/PimEyes

    PimEyes is a facial recognition search website that allows users to identify all images on the internet of a person given a sample image. The website is owned by EMEARobotics, a corporation based in Dubai. The owner and CEO of EMEARobotics and PimEye is Giorgi Gobronidze, who is based in Tbilisi, Georgia. [1]

  6. Object detection - Wikipedia

    en.wikipedia.org/wiki/Object_detection

    Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]

  7. Content-based image retrieval - Wikipedia

    en.wikipedia.org/wiki/Content-based_image_retrieval

    General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).

  8. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Since 7 October 2024, Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bug fixes (as opposed to just for security) and Python 3.9, [55] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.

  9. Speeded up robust features - Wikipedia

    en.wikipedia.org/wiki/Speeded_up_robust_features

    Filtering the image with a square is much faster if the integral image is used: (,) = = = (,) The sum of the original image within a rectangle can be evaluated quickly using the integral image, requiring evaluations at the rectangle's four corners.