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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.
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 ...
Python Features a full user interface and has a command-line tool for automatic operations. Has its own segmentation algorithm but uses system-wide OCR engines like Tesseract or Ocrad
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.
Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation.It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8]
The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008.
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On average only 0.01% of all sub-windows are positive (faces) Equal computation time is spent on all sub-windows; Must spend most time only on potentially positive sub-windows. A simple 2-feature classifier can achieve almost 100% detection rate with 50% FP rate. That classifier can act as a 1st layer of a series to filter out most negative windows