A facial recognition device is one that views an image or video of a person and compares it to one that is in the database. It does this by comparing structure, shape and proportions of the face; distance between the eyes, nose, mouth and jaw; upper outlines of the eye sockets; the sides of the mouth; location of the nose and eyes; and the area surrounding the check bones.
Upon enrolment in a facial recognition program, several pictures are taken of the subject at different angles and with different facial expressions. At time of verification and identification the subject stands in front of the camera for a few seconds, and then the image is compared to those that have been previously recorded.
To prevent a subject from using a picture or mask when being scanned in a facial recognition program, some security measures have been put into place. When the user is being scanned, they may be asked to blink, smile or nod their head. Another security feature would be the use of facial thermography to record the heat in the face.
The main facial recognition methods are: feature analysis, neural network, eigenfaces, automatic face processing.
Some facial recognition software algorithms identify faces by extracting features from an image of a subject’s face. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that can be used for facial recognition. A probe image is then compared with the face data.
A fairly new method on the market is three-dimensional facial recognition. This method uses 3-D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the face, such as the contour of eye sockets, nose and chin.
The advantages of 3-D facial recognition are that it is not affected by changes in lighting, and it can identify a face from a variety of angles, including profile view.
Another new technique in facial recognition uses the visual details of the skin, as captured in standard digital or scanned images. This technique is called skin texture analysis, turns the unique lines, patterns, and spots apparent in a person’s skin into a mathematical space. Preliminary tests have shown that using skin texture analysis in facial recognition can increase performance in identification by 20 to 25 percent.
The benefits of facial recognition are that it is not intrusive, can be done from a distance even without the user being aware they are being scanned. (i.e.: bank or government office)
What sets apart facial recognition from other biometric techniques is that it can be used for surveillance purposes; as in searching for wanted criminals, suspected terrorists, and missing children. Facial recognition can be done from far away so with no contact with the subject so they are unaware they are being scanned.
Facial recognition is most beneficial to use for facial authentication than for identification purposes, as it is too easy for someone to alter their face, features with a disguise or mask, etc. Environment is also a consideration as well as subject motion and focus on the camera.
Facial recognition, when used in combination with another biometric method, can improve verification and identification results dramatically.
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