Advantage and disadvantages of blue eyes technology with explanations
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Blue Eyes technology is based of the BlueEyes research project that started in 1997. IBM was said to have sold out this software since the early 2000‘s, but this has never been confirmed. However the present biometrics industry is modeled off of this reaserch project. That is why biometric technology is often referred to as Blue Eyes technology.
Without access to the original Blue Eyes Software, one cannot determine how Blue Eyes differs from more reccent biometric Software. What can be determined is the advantages and disadvantages of biometric technology at its current stage.
Advantages:
Biometric data cannot be falsified (although it can be misread)
Biometric data cannot be changed.
Biometrics can be more efficent with certain applications (i.e. statistical surveys)
Disadvantages:
All present biometric systems have FAR (false-acceptence-rate) and a FRR (false-regognition-rate). In basic security application both these rates have been much studied and error has been reduced, but not eliminated. High FAR biometric security systems will generally have a higher FRR. When the object being scanned is stationary or near stationary and can scanned multiple times this is more of nucence, but when the object being scanned is moving the system loses effectiveness. But this is not a priority when scanning is done intentionally as with employee entry or clock-in applications.
In biometric systems which collecting information on consumers, a low FFR is essential. When the system mesures certain quantites to model consumer behavior it does so generally. Unlike with a security entry system, varying imput can equal the same output. A Single negative in a binary sequence representing a set of Input quantities would never cause a positive output. Thus FAR is less of a concern. When is nessecary is for more data to be collected so correprate surveys of conosumer behavior can be accurate. This is why statistical applications of biometrics are engineered for low FFR. (This is based on a biometric statistical survey where data is only collected if one or more of a set of outputs is positive.)
Systems that identify objects in motion fall in between. These systems need a balence of FAR and FFR. Taking negative input as positive has to be minimized because they look for an exact match with data already in the system. Equally, taking positive input as negative also has to be minimized because the time to scan is limited. These systems are more difficult to optimize because they depend on finding the equilibrium between FAR and FRR.
As the technolgy develops many of these disadvantages may change, but are a key consideration for biometric technology.
Without access to the original Blue Eyes Software, one cannot determine how Blue Eyes differs from more reccent biometric Software. What can be determined is the advantages and disadvantages of biometric technology at its current stage.
Advantages:
Biometric data cannot be falsified (although it can be misread)
Biometric data cannot be changed.
Biometrics can be more efficent with certain applications (i.e. statistical surveys)
Disadvantages:
All present biometric systems have FAR (false-acceptence-rate) and a FRR (false-regognition-rate). In basic security application both these rates have been much studied and error has been reduced, but not eliminated. High FAR biometric security systems will generally have a higher FRR. When the object being scanned is stationary or near stationary and can scanned multiple times this is more of nucence, but when the object being scanned is moving the system loses effectiveness. But this is not a priority when scanning is done intentionally as with employee entry or clock-in applications.
In biometric systems which collecting information on consumers, a low FFR is essential. When the system mesures certain quantites to model consumer behavior it does so generally. Unlike with a security entry system, varying imput can equal the same output. A Single negative in a binary sequence representing a set of Input quantities would never cause a positive output. Thus FAR is less of a concern. When is nessecary is for more data to be collected so correprate surveys of conosumer behavior can be accurate. This is why statistical applications of biometrics are engineered for low FFR. (This is based on a biometric statistical survey where data is only collected if one or more of a set of outputs is positive.)
Systems that identify objects in motion fall in between. These systems need a balence of FAR and FFR. Taking negative input as positive has to be minimized because they look for an exact match with data already in the system. Equally, taking positive input as negative also has to be minimized because the time to scan is limited. These systems are more difficult to optimize because they depend on finding the equilibrium between FAR and FRR.
As the technolgy develops many of these disadvantages may change, but are a key consideration for biometric technology.
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