fingerprints recognition using neural networks
DESCRIPTION
it describes an algorithm in literature for fingerprints recognition using neural networksTRANSCRIPT
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
2Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
3Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
4Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
5Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
6Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
7Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
8Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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
9Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
Preprocessing system
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
10Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
Preprocessing system
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..
11Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
Preprocessing system
To remedy this problem, smoothing is necessary and includes: Filling holes Deleting redundant points Removing noisy points Filling potential missing points
12Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
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.
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Feature extraction and selection
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)
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Feature extraction and selection
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
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Feature extraction and selection
the network is trained by using the backpropagation learning technique and the weight change is updated according to
16Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
Feature extraction and selection
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
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Distribution of minutiae of two identical fingerprints 2(a) before and 2(b) after applying the rules
Feature extraction and selection
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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
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Invariant recognition
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.
20Alessandro Baffa 682075 - "Fingerprints recognition using Neural Network"
Invariant recognition
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
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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
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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