Humans are very good at recognizing faces and if computers complex patterns. Even a passage of time doesn't affect this capability and therefore it would help become as robust as humans in face recognition. Machine recognition of human faces from still or video images has attracted a great deal of attention in the psychology, image processing, pattern recognition, neural science, computer security, and computer vision communities. Face recognition is probably one of the most non-intrusive and user-friendly biometric authentication methods currently available; a screensaver equipped with face recognition technology can automatically unlock the screen whenever the authorized user approaches the computer.
Face is an important part of who we are and how people identify us. It is arguably a person's most unique physical characteristic. While humans have had the innate ability to recognize and distinguish different faces for millions of years, computers are just now catching up.
Visionics, a company based in New Jersey, is one of many developers of facial recognition technology. The twist to its particular software, FaceIt, is that it can pick someone's face out of a crowd, extract that face from the rest of the scene and compare it to a database full of stored images. In order for this software to work, it has to know what a basic face looks like. Facial recognition software is designed to pinpoint a face and measure its features. Each face has certain distinguishable landmarks, which make up the different facial features. These landmarks are referred to as nodal points. There are about 80 nodal points on a human face. Here are a few of the nodal points that are measured by the software:
Distance between eyes
" Width of nose
" Depth of eye sockets
" Cheekbones
" Jaw line
" Chin
These nodal points are measured to create a numerical code, a string of numbers that represents the face in a database. This code is called a faceprint. Only 14 to 22 nodal points are needed for the FaceIt software to complete the recognition process.
Software
Facial recognition software falls into a larger group of technologies known as biometrics. Biometrics uses biological information to verify identity. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. Besides facial recognition, biometric authentication methods also include:
" Fingerprint scan
" Retina scan
" Voice identification
Facial recognition methods generally involve a series of steps that serve to capture, analyze and compare a face to a database of stored images. The basic processes used by the FaceIt system to capture and compare images are:
Detection - When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
2. Alignment - Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
3. Normalization -The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
4. Representation - The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
Face is an important part of who we are and how people identify us. It is arguably a person's most unique physical characteristic. While humans have had the innate ability to recognize and distinguish different faces for millions of years, computers are just now catching up.
Visionics, a company based in New Jersey, is one of many developers of facial recognition technology. The twist to its particular software, FaceIt, is that it can pick someone's face out of a crowd, extract that face from the rest of the scene and compare it to a database full of stored images. In order for this software to work, it has to know what a basic face looks like. Facial recognition software is designed to pinpoint a face and measure its features. Each face has certain distinguishable landmarks, which make up the different facial features. These landmarks are referred to as nodal points. There are about 80 nodal points on a human face. Here are a few of the nodal points that are measured by the software:
Distance between eyes
" Width of nose
" Depth of eye sockets
" Cheekbones
" Jaw line
" Chin
These nodal points are measured to create a numerical code, a string of numbers that represents the face in a database. This code is called a faceprint. Only 14 to 22 nodal points are needed for the FaceIt software to complete the recognition process.
Software
Facial recognition software falls into a larger group of technologies known as biometrics. Biometrics uses biological information to verify identity. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. Besides facial recognition, biometric authentication methods also include:
" Fingerprint scan
" Retina scan
" Voice identification
Facial recognition methods generally involve a series of steps that serve to capture, analyze and compare a face to a database of stored images. The basic processes used by the FaceIt system to capture and compare images are:
Detection - When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
2. Alignment - Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
3. Normalization -The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
4. Representation - The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
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