Since the vast majority of fingerprint matching algorithms rely on minutiae matching, minutiae information are regarded as highly significant features for Automatic Fingerprint Recognition System. The accuracy of the AFRS system depends on the image quality, image enhancement methods, feature set extraction algorithms, and feature set pre. Fingerprint recognition: 18 November, 2016. Emka explains the need for tighter security in data centres through the use of biometric technology at the server View Detail. Fingerprint biometric technology from Neurotechnology gains second place in MINEX rankings for fingerprint matching algorithms thereby meeting US public sector.
Applies to:
- Windows 10
Windows Hello is the biometric authentication feature that helps strengthen authentication and helps to guard against potential spoofing through fingerprint matching and facial recognition.
Note
When Windows 10 first shipped, it included Microsoft Passport and Windows Hello, which worked together to provide multi-factor authentication. To simplify deployment and improve supportability, Microsoft has combined these technologies into a single solution under the Windows Hello name. Customers who have already deployed these technologies will not experience any change in functionality. Customers who have yet to evaluate Windows Hello will find it easier to deploy due to simplified policies, documentation, and semantics.
Because we realize your employees are going to want to use this new technology in your enterprise, we’ve been actively working with the device manufacturers to create strict design and performance recommendations that help to ensure that you can more confidently introduce Windows Hello biometrics into your organization.
How does Windows Hello work?
Windows Hello lets your employees use fingerprint or facial recognition as an alternative method to unlocking a device. With Windows Hello, authentication happens when the employee provides his or her unique biometric identifier while accessing the device-specific Windows Hello credentials.
The Windows Hello authenticator works to authenticate and allow employees onto your enterprise network. Authentication doesn’t roam among devices, isn’t shared with a server, and can’t easily be extracted from a device. If multiple employees share a device, each employee will use his or her own biometric data on the device.
Why should I let my employees use Windows Hello?
Windows Hello provides many benefits, including:
It helps to strengthen your protections against credential theft. Because an attacker must have both the device and the biometric info or PIN, it’s much more difficult to gain access without the employee’s knowledge.
Employees get a simple authentication method (backed up with a PIN) that’s always with them, so there’s nothing to lose. No more forgetting passwords!
Support for Windows Hello is built into the operating system so you can add additional biometric devices and polices as part of a coordinated rollout or to individual employees or groups using Group Policy or Mobile Device Management (MDM) configurations service provider (CSP) policies.
For more info about the available Group Policies and MDM CSPs, see the Implement Windows Hello for Business in your organization topic.
Where is Microsoft Hello data stored?
The biometric data used to support Windows Hello is stored on the local device only. It doesn’t roam and is never sent to external devices or servers. This separation helps to stop potential attackers by providing no single collection point that an attacker could potentially compromise to steal biometric data. Additionally, even if an attacker was actually able to get the biometric data, it still can’t be easily converted to a form that could be recognized by the biometric sensor.
Has Microsoft set any device requirements for Windows Hello?
We’ve been working with the device manufacturers to help ensure a high-level of performance and protection is met by each sensor and device, based on these requirements:
False Accept Rate (FAR). Represents the instance a biometric identification solution verifies an unauthorized person. This is normally represented as a ratio of number of instances in a given population size, for example 1 in 100 000. This can also be represented as a percentage of occurrence, for example, 0.001%. This measurement is heavily considered the most important with regards to the security of the biometric algorithm.
False Reject Rate (FRR). Represents the instances a biometric identification solution fails to verify an authorized person correctly. Usually represented as a percentage, the sum of the True Accept Rate and False Reject Rate is 1. Can be with or without anti-spoofing or liveness detection.
Fingerprint sensor requirements
To allow fingerprint matching, you must have devices with fingerprint sensors and software. Fingerprint sensors, or sensors that use an employee’s unique fingerprint as an alternative log on option, can be touch sensors (large area or small area) or swipe sensors. Each type of sensor has its own set of detailed requirements that must be implemented by the manufacturer, but all of the sensors must include anti-spoofing measures (required).
Acceptable performance range for small to large size touch sensors
False Accept Rate (FAR): <0.001 – 0.002%
Effective, real world FRR with Anti-spoofing or liveness detection: <10%
Acceptable performance range for swipe sensors
False Accept Rate (FAR): <0.002%
Effective, real world FRR with Anti-spoofing or liveness detection: <10%
Facial recognition sensors
To allow facial recognition, you must have devices with integrated special infrared (IR) sensors and software. Facial recognition sensors use special cameras that see in IR light, letting them tell the difference between a photo and a living person while scanning an employee’s facial features. These sensors, like the fingerprint sensors, must also include anti-spoofing measures (required) and a way to configure them (optional).
False Accept Rate (FAR): <0.001%
False Reject Rate (FRR) without Anti-spoofing or liveness detection: <5%
Effective, real world FRR with Anti-spoofing or liveness detection: <10%
Related topics
I am saving the fingerprints in a field 'blob', then wonder if the only way to compare these impressions is retrieving all prints saved in the database and then create a vector to check, using the function 'identify_finger'? You can check directly from the database using a SELECT?
I'm working with libfprint. In this code the verification is done in a vector:
3 Answers
There are two fundamentally different ways to use a fingerprint database. One is to verify the identity of a person who is known through other means, and one is to search for a person whose identity is unknown.
A simple library such as libfprint
is suitable for the first case only. Since you're using it to verify someone you can use their identity to look up a single row from the database. Perhaps you've scanned more than one finger, or perhaps you've stored multiple scans per finger, but it will still be a small number of database blobs returned.
A fingerprint search algorithm must be designed from the ground up to narrow the search space, to compare quickly, and to rank the results and deal with false positives. Just as a Google search may come up with pages totally unrelated to what you're looking for, so too will a fingerprint search. There are companies that devote their entire existence to solving this problem.
Mark RansomMark RansomAnother way would be to have a mysql plugin that knows how to work with fingerprint images and select based on what you are looking for.
I really doubt that there is such a thing.
You could also try to parallelize the fingerprint comparation, ie - calling:
in parallel, on different cores/machines
Tudor ConstantinTudor ConstantinYou can't check directly from the database using a SELECT because each scan is different and will produce different blobs. libfprint does the hard work of comparing different scans and judging if they are from the same person or not
What zinking and Tudor are saying, I think, is that if you understand how does that judgement process works (which is by the way, by minutiae comparison) you can develop a method of storing the relevant data for the process (the *minutiae, maybe?) in the database and then a method for fetching the relevant values -- maybe a kind of index or some type of extension to the database.
In other words, you would have to reimplement the libfprint
algorithms in a more complex (and beautiful) way, instead of just accepting the libfprint
method of comparing the scan with all stored fingerprint in a loop.
other solutions for speeding your program
use C:
I only know sufficient C to write kind of hello-world programs, but it was not hard to write code in pure C to use the fp_identify_finger_img
function of libfprint
and I can tell you it is much faster than pyfprint.identify_finger
.
You can continue doing the enrollment part of the stuff in python. I do it.
use a time / location based SELECT:
If you know your users will scan their fingerprints with more probability at some time than other time, or at some place than other place (maybe arriving at work at some time and scanning their fingers, or leaving, or entering the building by one gate, or by other), you can collect data (at each scan) for measuring the probabilities and creating parallel tables to sort the users for their probability of arriving at each time and location.
We know that identify_finger
tries to identify fingers in a loop with the fingerprint objects you provided in a list, so we can use that and give it the objects sorted in a way in which the more likely user for that time and that location will be the first in the list and so on.