What is the difference between "biometric identification" and "biometric verification"?
Biometrics are used for different purposes, but they are generally part of either a verification system or an identification system. The differences between these two types of systems can make a difference in how quickly the system operates and how accurate it is as the size of a biometric database increases.
Verification Systems
Verification systems seek to answer the question “Is this person who they say they are?” Under a verification system, an individual presents himself or herself as a specific person. The system checks his or her biometric against a biometric profile that already exists in the database linked to that person’s file in order to find a match.
Verification systems are generally described as a 1-to-1 matching system because the system tries to match the biometric presented by the individual against a specific biometric already on file.
Because verification systems only need to compare the presented biometric to a biometric reference stored in the system, they can generate results more quickly and are more accurate than identification systems, even when the size of the database increases.
Identification Systems
Identification systems are different from verification systems because an identification system seeks to identify an unknown person, or unknown biometric. The system tries to answer the questions “Who is this person?” or “Who generated this biometric?” and must check the biometric presented against all others already in the database. Identification systems are described as a 1-to-n matching system, where n is the total number of biometrics in the database. Forensic databases, where a government tries to identify a latent print or DNA discarded at a crime scene, often operate as identification systems.
one-to-one comparison, biometric verification systems are generally much faster than biometric identification systems. Most commercial applications of biometrics for time and attendance or access control use biometric verification.
Biometrics are used for different purposes, but they are generally part of either a verification system or an identification system. The differences between these two types of systems can make a difference in how quickly the system operates and how accurate it is as the size of a biometric database increases.
Verification Systems
Verification systems seek to answer the question “Is this person who they say they are?” Under a verification system, an individual presents himself or herself as a specific person. The system checks his or her biometric against a biometric profile that already exists in the database linked to that person’s file in order to find a match.
Verification systems are generally described as a 1-to-1 matching system because the system tries to match the biometric presented by the individual against a specific biometric already on file.
Because verification systems only need to compare the presented biometric to a biometric reference stored in the system, they can generate results more quickly and are more accurate than identification systems, even when the size of the database increases.
Identification Systems
Identification systems are different from verification systems because an identification system seeks to identify an unknown person, or unknown biometric. The system tries to answer the questions “Who is this person?” or “Who generated this biometric?” and must check the biometric presented against all others already in the database. Identification systems are described as a 1-to-n matching system, where n is the total number of biometrics in the database. Forensic databases, where a government tries to identify a latent print or DNA discarded at a crime scene, often operate as identification systems.
one-to-one comparison, biometric verification systems are generally much faster than biometric identification systems. Most commercial applications of biometrics for time and attendance or access control use biometric verification.
12 comments:
Biometrics is one of the most commonly use log-in system for most companies now.
Biometrics is used as a form of identity access management and access control.
Bio-Key even outsources their pumping now.
BIO-key employs fingerprint biometrics to perform true user identification. When exploring biometric security or any other form of security, it is important to understand the difference between identification and verification.
Biometric identification technology allows users to prove their identity by submitting a biometric sample, such as a fingerprint, iris scan or voice pattern. No other identification data is provided; identification is achieved through biometrics alone.
Non-biometric technologies authorize users via a key, card or identification code such as a PIN or password. Biometric verification technology adds a biometric sample to the mix, along with the identification code or key. These systems can be defeated easily by obtaining or counterfeiting the key, card or password.
Among automated biometric systems, only those that are capable of real-time identification can eliminate the possibility of duplicates in a database.
Biometric identification compares a biometric "signature" to all the records stored in a database to determine if there is a match. Because it requires comparing each existing record in the database against the new biometric characteristic, it can be slow and is usually not suitable for real-time applications such as access control or time and attendance.
Can u explain PCA, LDA and ICA. I'm finding it difficult to grasp the gist of their mathematical statements. How are they use in face recognition? thanks
Linear discriminant analysis (LDA) and the related Fisher's linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events.
In computerised face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template. The linear combinations obtained using Fisher's linear discriminant are called Fisher faces, while those obtained using the related principal component analysis are called eigenfaces.
Independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals. It is a special case of blind source separation.
Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD).
the advantages of PCA?
It is fast, and it only needs a small amount of memory. PCA basically performs dimensionality reduction.
So we have to perform various pre-processing steps in order to utilize the PCA method for recognition. Some may be:
Face detection and extraction: Detect the face using the OpenCV face detector, and then recognize the face.
Illumination Normalization:: It is important to apply face pre-processing, such as Histogram Equalization that is easy to do with OpenCV or use other methods.
Scaling: Make sure all face images get scaled to the same size.
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