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Fingerprints Recognition          using    Neural Network         Presented By:         GURJANT SINGH SANDHU (328)        ...
Fingerprints A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger. Their patte...
Fingerprint Pattern
Pattern recognition System      Image         Edge                  Feature    Classifier                              Thi...
Pattern recognition System      Image         Edge                  Feature    Classifier                              Thi...
Why use Neural Network?    A neural network consists of an interconnected group    of artificial neurons, and it processe...
Other Applications Character Recognition  The idea of character recognition has become veryimportant as handheld devices ...
Other Applications Stock Market Prediction  The day-to-day business of stock market isextremely complicated. Stock prices...
Preprocessing System   The first phase of the work is to capture the    fingerprints image and convert it into a digital ...
Preprocessing System   After obtaining the binary form of the    fingerprint image, there may be some    irregularities c...
Application of fingerprint                   Recognition   The fingerprint recognition system can be easily    embedded i...
Criticism   Despite the widespread acceptance of fingerprint    evidence, many question its worth due to a significant   ...
Conclusion For centuries fingerprints have been one of the most  highly used methods for human recognition; automated  bi...
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Fingerprints recognition

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Fingerprints recognition

  1. 1. Fingerprints Recognition using Neural Network Presented By: GURJANT SINGH SANDHU (328) PRANJAL SINGH (333)
  2. 2. Fingerprints A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger. Their pattern is permanent and unchangeable on each finger during the whole life time of an individual. The probability that fingerprints of two individual are alike is about 1 in 1.9×1015. According to FBI the accuracy and reliability of fingerprint scans are correct 99.8% of the time.
  3. 3. Fingerprint Pattern
  4. 4. Pattern recognition System Image Edge Feature Classifier Thinning Acquisition Detection Extractor Image Acquisition Converting a scene into an array of numbers that can be manipulated by a computer. Edge Detection and Thinning These are the part of preprocessing step which involvesremoving noise, enhancing the picture and, if necessary,segmenting the image into meaningful regions.
  5. 5. Pattern recognition System Image Edge Feature Classifier Thinning Acquisition Detection Extractor Feature extraction The image is represented by a set ofnumerical “features” to removeredundancy from data and reduce its dimensions. Classification Class label is assigned to the image by examining itsextracted features and comparing them with the class thatit has already learned.
  6. 6. Why use Neural Network? A neural network consists of an interconnected group of artificial neurons, and it processes information and help us to find solution. There is no need to program Neural Network they learn with the examples. Neural Networks offers significant speed advantage over conventional techniques.
  7. 7. Other Applications Character Recognition The idea of character recognition has become veryimportant as handheld devices like Palm Pilot arebecoming increasingly popular. Image Compression Neural networks can receive and process largeamount of information at once, making them useful inimage compression. With internet explosion andmore and websites using more and more images,using neural networks for image compression isworth a look.
  8. 8. Other Applications Stock Market Prediction The day-to-day business of stock market isextremely complicated. Stock prices will go up ordown is the result of many different factors. Sinceneural network can examine a lot of information, theycan be used to predict stock prices. Travelling Salesman Problem Neural network can solve the travelling salesmanproblem, but only to a certain degree ofapproximation. Medicine, Security, and Loan Applications.
  9. 9. Preprocessing System The first phase of the work is to capture the fingerprints image and convert it into a digital representation of 512×512 by 256 grey levels. The binary image is further enhanced by a thinning algorithm which reduces the image ridges to a skeletal structure.
  10. 10. Preprocessing System After obtaining the binary form of the fingerprint image, there may be some irregularities caused by skinfolds and spreading of ink due to finger pressure, and so on… The remedy to this problem is smoothing to fill holes, delete unnecessary points, removing noisy points and filling necessary missing points.
  11. 11. Application of fingerprint Recognition The fingerprint recognition system can be easily embedded in any system. It is used in- ◦ Recognition of criminals in law enforcement. ◦ Used in providing security to cars, lockers, banks, shops. ◦ To differentiate between persons. ◦ To count the individuals. ◦ Drug detection.
  12. 12. Criticism Despite the widespread acceptance of fingerprint evidence, many question its worth due to a significant amount of identification mistakes. There is a question of its reliability and accuracy. For example, in 2000, an individual was arrested for murder and was told by police that fingerprint experts matched his fingerprints to those found at the crime scene. The individuals attorney hired his own fingerprint experts, two former FBI examiners, who determined that absolutely no positive identification took place. After some post-incarceration legal wrangling, this evidence was deemed sufficient for an acquittal.
  13. 13. Conclusion For centuries fingerprints have been one of the most highly used methods for human recognition; automated biometric system have only been available in recent years. The advancement of technology have led to next generation of fingerprint recognition devices which are highly reliable and accurate. Fingerprints have a broad acceptance with the general public, law enforcement and the forensic science community. Hence, they will continue to be used for human recognition and for new systems that require a reliable biometric.
  14. 14. THANK YOU
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