About Face Recognition
Introduction
Biological Recognition Technology is also called Biometrics. It is used to authenticate the identity by image processing and face recognition based on the human body's unique and measurable physical characteristics (such as fingerprints, irises, faces, vein patterns) and behavioral characteristics (such as signatures, voiceprints, keystrokes).The Biological Recognition System consists of two basic logical modules:
1. Enrollment Module
2. Identification Module
The flow chart of face recognition is shown as follows:
Enrollment -----Biometric Sensor -----Feature Extractor ---database
Identification ---- Biometric Sensor ----Feature Extractor / Feature Matcher
In the Enrollment Module, after user's enrollment, the Biometric Sensor captures the characteristics which are transformed into a digital code in order to be matched easily and stored economically. Then the encoding is further abstracted in the Feature Extractor, so a template is created which can be stored in the system database, magnetic card or IC card.
In the Identification Module, after the Biometric Sensor captures user's characteristics which are transformed into a digital code and then the encoding is further abstracted, the user's biological image is generated. Then the Feature Matcher matches it against the pre-coded biological images stored in the database to authenticate the identity of the user.
Take Face Recognition as an example, the basic steps are as follows:
1. After a user's enrollment, system captures the user's face image with a standard camera or from photographs and creates a template with a digital code which can be stored in the database.
2. When identifying a user, system captures the face image with a standard camera and abstracts the characteristics.
3. System identifies and matches user's coded face image against the pre-coded ones stored in the database.
4. System authenticates the identity of the user or display the close matches to choose from.
Comparison of Different Recognition Technologies
The biological recognition has a very promising future for public use. Usually there are two different ways to resolve a person's identity: verification and identification. Verification (Am I whom I claim I am?) involves confirming or denying a person's claimed identity. In identification, one has to establish a person's identity (Who am I?).
Application Areas of Different Recognition Technologies
DNA: Identification in Law, Medicine, Heredity
Fingerprint:Identification / Verification in Immigration, Insurance, Military, Access Control
Face: Identification / Verification in Suspects' Identification, Missing Persons' Recovery, Access Control, Credit Card
Signature: Identification / Verification in Signature Verification
Iris: Identification in Access Control
Voice: Identification / Verification in Telephone Service, Access Control
Face Recognition Technology is a method used to authenticate the identity of individuals by face image processing and recognition based on face features. It is able to capture face information via a camera or video stream, analyze and extract face features. Then the captured face information will be compared with the templates pre-stored in the system database. The identity of the person will be recognized immediately.
Face Recognition Technology is an important branch of Biometric Technology. It has two obvious advantages. One is that it happens automatically. That is, the unidentified person needn't contact the recognition device, and face recognition process is still going on continuously or without being noticed. The other is its viewable feature that means it is easy to know "who are you" by face image.
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Title: About FIRS's Face Recognition Technology
Introduction:DLFA (Dynamic Local Feature Analysis) is a modern and effective face recognition technology. First of all, It processes the face image to get rid of the useless information. Then the face image is transformed into a binary one by border adaptation detection. Then the skin texture, namely skinprint, is abstracted, Local Feature Analysis (LFA) is used to process the border shadow and the skinprint to identify the face image
Local Feature Analysis (LFA) is a complex mathematical algorithm used to compare images of two human faces, similar to toy building blocks . LAF is based on the fact that the face consists of many small feature segments with a simple structure. LFA identifies a face with from32 to 50 segments which are the most commonly selected ones including the nose, the eyes, the mouth and the specific skeleton curvature difference, like cheeks'. All these segments are transformed into data through complicated but efficient mathematical formulae. They represent the whole face, usually the shape of a common face, not the facial features. Face recognition depends on not only the specific segments but also their geometric structure (such as the shape and related position). In this way, LFA can perform face recognition after the facial features are transformed into data through complicated but efficient mathematical formulae.
DLFA which can minimize the impact of background light intensity in the environment captures the facial information, and the skinprint provides the exact details of facial features. After combining the two pieces of information, Local Feature Analysis (LFA) Algorithm is used to compare and count the 173 characteristic points on the face. Whenever it is in enrollment or identification, the face recognition algorithm can achieve a very high degree of accuracy, identifying a person from a million people accurately
Introduction:DLFA (Dynamic Local Feature Analysis) is a modern and effective face recognition technology. First of all, It processes the face image to get rid of the useless information. Then the face image is transformed into a binary one by border adaptation detection. Then the skin texture, namely skinprint, is abstracted, Local Feature Analysis (LFA) is used to process the border shadow and the skinprint to identify the face image
Local Feature Analysis (LFA) is a complex mathematical algorithm used to compare images of two human faces, similar to toy building blocks . LAF is based on the fact that the face consists of many small feature segments with a simple structure. LFA identifies a face with from32 to 50 segments which are the most commonly selected ones including the nose, the eyes, the mouth and the specific skeleton curvature difference, like cheeks'. All these segments are transformed into data through complicated but efficient mathematical formulae. They represent the whole face, usually the shape of a common face, not the facial features. Face recognition depends on not only the specific segments but also their geometric structure (such as the shape and related position). In this way, LFA can perform face recognition after the facial features are transformed into data through complicated but efficient mathematical formulae.
DLFA which can minimize the impact of background light intensity in the environment captures the facial information, and the skinprint provides the exact details of facial features. After combining the two pieces of information, Local Feature Analysis (LFA) Algorithm is used to compare and count the 173 characteristic points on the face. Whenever it is in enrollment or identification, the face recognition algorithm can achieve a very high degree of accuracy, identifying a person from a million people accurately
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Company Info
FIRS Intelligent Technology (Shenzhen) Co., Ltd.
[China]
[Verified Member]
City: Shenzhen
Province/State: Guangdong
Country/Region : China
Business Type:Manufacturer
Online Postings: Products