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  2. FaceNet - Wikipedia

    en.wikipedia.org/wiki/FaceNet

    For a discussion on the vulnerabilities of Facenet-based face recognition algorithms in applications to the Deepfake videos: Pavel Korshunov; Sébastien Marcel (2022). "The Threat of Deepfakes to Computer and Human Visions" in: Handbook of Digital Face Manipulation and Detection From DeepFakes to Morphing Attacks (PDF). Springer. pp. 97– 114.

  3. Eigenface - Wikipedia

    en.wikipedia.org/wiki/Eigenface

    Eigenface provides an easy and cheap way to realize face recognition in that: Its training process is completely automatic and easy to code. Eigenface adequately reduces statistical complexity in face image representation. Once eigenfaces of a database are calculated, face recognition can be achieved in real time.

  4. Face detection - Wikipedia

    en.wikipedia.org/wiki/Face_detection

    It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. [3] A reliable face-detection approach based on the genetic algorithm and the eigen-face [4] technique:

  5. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    Facial recognition algorithms can help in diagnosing some diseases using specific features on the nose, cheeks and other part of the human face. [75] Relying on developed data sets, machine learning has been used to identify genetic abnormalities just based on facial dimensions. [76] FRT has also been used to verify patients before surgery ...

  6. DeepFace - Wikipedia

    en.wikipedia.org/wiki/DeepFace

    DeepFace is a deep learning facial recognition system created by a research group at Facebook.It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.

  7. Viola–Jones object detection framework - Wikipedia

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

    Our task is to make a binary decision: whether it is a photo of a standardized face (frontal, well-lit, etc) or not. Viola–Jones is essentially a boosted feature learning algorithm, trained by running a modified AdaBoost algorithm on Haar feature classifiers to find a sequence of classifiers ,,...,. Haar feature classifiers are crude, but ...

  8. FindFace - Wikipedia

    en.wikipedia.org/wiki/FindFace

    FindFace is a face recognition technology developed by the Russian company NtechLab that specializes in neural network tools. The company provides a line of services for the state and various business sectors based on FindFace algorithm.

  9. Face Recognition Vendor Test - Wikipedia

    en.wikipedia.org/wiki/Face_Recognition_Vendor_Test

    The FRGC was a separate algorithm development project designed to promote and advance face recognition technology that supports existing face recognition efforts in the U.S. Government. One of the objectives of the FRGC was to develop face recognition algorithms capable of performance an order of magnitude better than FRVT 2002.