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In order to work, face detection applications use machine learning and formulas known as algorithms to detecting human faces within larger images.These larger images might contain numerous objects that aren’t faces such as landscapes, buildings and other parts of humans (e.g. Face detection is a broader term than face recognition.This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories.
Face detection just means that a system is able to identify that there is a human face present in an image or video.
Face detection has several applications, only one of which is facial recognition.
In this thesis, we divide the history of face recognition into four stages and list the main problems and algorithms at each stage.
We find that face recognition in unconstrained environments is still an unsolved problem although many face recognition algorithms have been proposed in the last decade. First, many methods do not perform well when tested in uncontrolled databases even when all the faces are close to frontal.
It actually attempts to establish whose face it is.
The process works using a computer application that captures a digital image of an individual’s face (sometimes taken from a video frame) and compares it to images in a database of stored records.
Banks, retail stores, stadiums, airports and other facilities use facial recognition to reduce crime and prevent violence.
So in short, while all facial recognition systems use face detection, not all face detection systems have a facial recognition component.
In our industry, the terms face detection and face recognition are sometimes used interchangeably. To help clear things up, let’s take a look at the term face detection and how it differs from the term face recognition.
The definition of face detection refers to computer technology that is able to identify the presence of people’s faces within digital images.