How machine learning works in Mobile Fingerprint sensor

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.

Machine Learning works in Mobile Fingerprint sensor

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.

machine learning in fingerprint sensor

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 


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.

Important Links

Home Page 

Courses Link  

  1. Python Course  
  2. Machine Learning Course 
  3. Data Science Course 
  4. Digital Marketing Course  
  5. Python Training in Noida 
  6. ML Training in Noida 
  7. DS Training in Noida 
  8. Digital Marketing Training in Noida 
  9. Winter Training 
  10. DS Training in Bangalore 
  11. DS Training in Hyderabad  
  12. DS Training in Pune 
  13. DS Training in Chandigarh/Mohali 
  14. Python Training in Chandigarh/Mohali 
  15. DS Certification Course 
  16. DS Training in Lucknow 
  17. Machine Learning Certification Course 
  18. Data Science Training Institute in Noida
  19. Business Analyst Certification Course 
  20. DS Training in USA 
  21. Python Certification Course 
  22. Digital Marketing Training in Bangalore
  23. Internship Training in Noida
  24. ONLEI Technologies India 

Leave a Comment

Your email address will not be published. Required fields are marked *