Fingerprints recognition using neural networks

1Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"

Fingerprints Fingerprints recognition using recognition using neural networkneural network

Politecnico di Milano Polo Regionale di ComoPolitecnico di Milano Polo Regionale di ComoMethods and Technologies for Image Processing

Author: Alessandro BAFFA 682075


Agenda

Introduction Features of fingerprints The pattern recognition system Why using neural network? The goal of this method

Preprocessing system Feature extraction and selection Invariant recognition Result References


Features of fingerprints

Fingerprints are imprints formed by friction ridges of the skin in fingers and thumbs.

Their pattern are permanent and unchangeable on each finger during all the life;

They are individual (the probability that two fingerprints are alike is about 1 in 1.9x10^15) They have long been used for

identification


The pattern recognition system

Image acquisition converting a scene into an array of numbers that can be manipulated by a computer

Edge detection and thinning are parts of the preprocessing step which involves removing noise, enhancing the picture and, if necessary, segmenting the image into meaningful regions


Feature extraction in which the image is represented by a set of numerical “features” to remove redundancy from the data and reduce its dimension

Classification where a class label is assigned to the image/object by examining its extracted features and comparing them with the class that the classifier has learned during its training stage. The main focus of this method is on these two last parts

The pattern recognition system


Why using neural network?

Neural network enable solutions to be found to problems where algorithmic methods are too computationally intensive or do not exist

The problem of feature extraction and classification seems to be a suitable application for neural nets.

They offer significant speed advantages over conventional techniques


The goal of this method

This proposed method is based on a data model for fingerprints that is structural rather than coordinate.

This structural data model is robust with respect to traslation, rotation and distortion


Preprocessing system

The first phase of the work is to capture the fingerprints image and convert it to a digital representation of 512x512 by 256 gray levels.

Histogram equalization technique is used to increase the contrast if the illumination condition is poor

But we are only interested in binary information



Binarization is usually performed by using Laplacian edge detection operator Local derivative operator such as

“Roberts”, “Prewitt” or “Sobel” Thresholding tecnique

The binary image is further enhanced by a thinning algorithm which reduces the image ridges to a skeletal structure



The thinning algorithm while deleting unwanted points should not: Remove end points Break connectedness Cause excessive erosion of the region

After obtaining the binary form of the fingerprint image, there may be some irregularities caused by skinfolds and contiguous ridges or spreading of ink due to finger pressure, and so on..



To remedy this problem, smoothing is necessary and includes: Filling holes Deleting redundant points Removing noisy points Filling potential missing points


Feature extraction and selection

Selection of good feature is a crucial step in the process since the next stage sees only these features and acts upon them.

150 different minutiae type have been identified but in practice only ridge ending and ridge bifurcation are used.



Good features are those satisfying two requirements: Small intraclass invariance (i.e. slightly

different shapes with similar general characteristics should have numerically close values)

Large interclass separation (i.e. features from different classes should be quite different numerically)



A multilayer perceptron network of three layers is trained to detect the minutiae in the thinned part image of size 128x128 The first layer has nine units associated with the

components of the input vector The hidden layer has five units The output layer has one unit corresponding to the

number of the classes

The network is trained to output ‘1’ when the input window is centered on the feature to be located and it outputs ‘0’ if minutiae are not present



the network is trained by using the backpropagation learning technique and the weight change is updated according to



The trained network is then used to analyze the complete image by raster scanning the fingerprint via window of size 3x3

In order to prevent the falsely reported features and select “significant” minutiae, two more rules are added to the system to guarantee perfect ridge forks are detected while excluding all other features: At those potential minutiae feature points we examine

them by increasing the window size to 5x5 If two or more minutiae are too close togheter, we ignore

all of them


Distribution of minutiae of two identical fingerprints 2(a) before and 2(b) after applying the rules



Invariant recognition

The location of a reference point of the fingerprints is important for invariant recognition and has to be determined Contour tracing is used to find one or more

turning points (i.e. points with maximum rate of change of tracing movement) This points are then used to find the reference point



The Euclidean distance d(i) from each feature point i to the reference point are calculated The distance to the center confers the property of

positional invariance The data are then sorted in ascending order from d(0)

to d(N) this operation gives the data the property of rotational

invariance In order to make the data becomes invariant to scale

change, it is normalized to unity by the shortest distance d(0), i.e. dist(i) = d(0)/d(i), i = 0..N This will weight those feature points nearer to the center

more heavly because usually these points are more significant in classification.



The centroidal data patterns should be shift, scale and rotational independent

Also the invariant feature vectors are in the range [0,1] and they can be directly used as the training/stored vectors in the MLP classifier


Result

The recognition rate of fingerprints depends much on the quality of the fingerprints and effectiveness of the preprocessing system Such as the thresholding level used in edge

detection

If there are too many broken lines or noisy points in the image, the preprocessing system contour tracing may fail. An intelligent connection algorithm to recover

broken lines and suppress spurious irregularities is necessary


References

W.F. Leung – S.H. Leung, W.H. Lau – Andrew Luk, Fingerprints recognition using neural network

M.T. Leung – W.E. Engeler – P. Frank, Fingerprints image processing using neural network

Jacques de Villiers – Etienne Barnard, Backpropagation neural nets with one and two hidden layers

Andrew Luk – S.H. Leung – C.K. Lee – W.H. Lau, A two level classifier for fingerprint recognition

Business

Fingerprints recognition using neural networks