How machine learning works in Mobile Fingerprint sensor
Fingerprint method of identification is the oldest and widely used method of authentication used in biometrics. There are several reasons like displacement of finger during scanning, environmental conditions, behavior of user, etc., which causes the reduction in acceptance rate during fingerprint recognition. We are identified by using the traits and unique characteristics.These are our physiological responses.These characteristics can be features such as facial expressions, iris patterns, voice, and even fingerprints.Fingerprint-based authentication is the most popular one.Fingerprints have different patterns that make each individual fingerprint unique.
It uses an ultrasonic scanner instead of an optical one. Ultrasonic sensors work using ultrasound to build up an image of your fingerprint (yes, really) and work better with messy fingerprints – if your hands are wet or oily with sun cream, for example. They’re essentially ‘Face ID for your finger‘
Can fingerprint scanners be beaten?
The report says a fingerprint scanner can be “hacked” by using a picture of the target’s fingerprint, creating a negative in Photoshop, printing the resulting image, and then putting some wood glue on top of the imitated fingerprint so it can be used to trick many commercial scanners.
As so many conditions and factors play an important role in determining the final ridge pattern, we consider fingerprint patterns to be unique to each and every individual.
Fingerprints can be considered as a pattern of ridges and valleys. Its classification and verification is pattern based problems.Optical images of fingerprints can be classified based on the details of its ridge configuration.
The optical input that is captured from the fingerprint sensor is stored and accessed by the system in specific formats (e.g. .bmp, .jpeg, etc.) and we are using these captured inputs to train our neural network.
Some of the verified approaches to implement fingerprint:-
1)Pre-Classifier Convolution Neural Network
2)Data Samples
3)Inception Model
Conclusion:-
We use Machine Learning algorithms and its improvised version of technologies as a pre-verification filter to filter out bad or malicious fingerprints.The inception model is used for filtering out bad fingerprints.If the output of the inception model says that it is a good fingerprint, it is given to the verification module where matching of the fingerprint is performed.
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