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  2. Comparison of image viewers - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_image_viewers

    File renaming, single-click background copy/move to preset location, single-click rating/labeling (writes Adobe XMP sidecar files and/or embeds XMP metadata within JPEG/TIFF/HD Photo/JPEG XR), Windows rating, color management including custom target profile selection, Unicode support, Exif shooting data (shutter speed, f-stop, ISO speed ...

  3. 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]

  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

    Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition, fingerprint image acquisition, palm recognition or voice recognition, it is widely adopted due to its contactless process. [3] Facial recognition systems have been deployed in advanced human–computer interaction, video surveillance ...

  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. 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]

  8. 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.

  9. Category:Facial recognition - Wikipedia

    en.wikipedia.org/wiki/Category:Facial_recognition

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