Facial Recognition

Facial recognition is a technology which uses biometric markers to detect the identity of individuals. Specifically, this type of recognition system measures facial contours and compares them against previously uploaded images. By precisely comparing images, these recognition systems make it possible to determine the identity of individuals, and they can be used for a wide range of security, commercial, and even medical purposes.

Analyzing facial features is widely used in border control and security settings to prevent unauthorized entry into sensitive locations such as government buildings, corporate offices, or military installations. So it plays an ever more important role in securing society from harm. Facial identification is also favored in many cases because it does not require close proximity to the subject.

Until recently, the most common uses of facial identification systems have revolved around security and access. For example, it has been used by US border staff to speed up arrival checks, while the technology can also be used by corporations to control access to their facilities. However, its uses have started to go beyond security, offering exciting possibilities for commercial users. Medical organizations have used it to detect facial types which suggest vulnerability to certain conditions, and dating sites have also harnessed facial recognition to match people with similar faces. Analyzing facial contours can also be used to match faces against emotional templates – giving retailers an idea of how customers are responding to the products on offer, the behavior of staff, or the layout of the store.

Facial identification generally involves mapping “nodes” on an individual’s face to create what is known as a “faceprint.” This functions just like a fingerprint, and includes things like the position of an individual’s eyes and nose – creating a set of data which is unique to each person. Most face-based recognition systems use advanced cameras to capture images of subjects, which are then compared to existing faceprints on a centrally stored database. And with error rates below 1%, it’s one of the most accurate recognition options available, whether you use it for security or market research purposes.