Fingerprint recognition using Image processing

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

*They have long been used for identification because of their immutability and individuality.

*Immutability refers to the permanent and unchanging character of the pattern on each finger.

*Individuality refers to the uniqueness of ridge details across individuals; the probability that two fingerprints are alike is about 1 in 1.9x1015.

INTRODUCTION

Two approaches will be described for fingerprint recognition:-

• Approach 1: Based on minutiae located in a fingerprint

• Approach 2: Based on frequency content and ridge orientation of a fingerprint

First Approach :

*Most automatic systems for fingerprint comparison are based on minutiae matching

*Minutiae are local discontinuities in the fingerprint pattern. A total of 150 different minutiae types have been identified. In practice only ridge ending and ridge bifurcation

*minutiae types are used in fingerprint recognition. Examples of minutiae are shown in figures

(a) (b)Figure 1. (a) Different minutiae types, (b) Ridge ending & Bifurcation

The building blocks of a fingerprint recognition system are:-

ImageAcquisition

EdgeDetection Thinning Feature

ExtractorClassifier

PhysicalFingerprint

Classification Decision

Figure 2. Fingerprint recognition system

a) Image Acquisition

*A number of methods are used to acquire fingerprints. Among them, the inkedimpression method remains the most popular one. Inkless fingerprint scanners are also present eliminating the intermediate digitization process.

*Fingerprint quality is very important since it affects directly the minutiae extractionalgorithm. Two types of degradation usually affect fingerprint images:

1) the ridge lines arenot strictly continuous since they sometimes include small breaks (gaps);

2) parallel ridgelines are not always well separated due to the presence of cluttering noise. The resolution of the scanned fingerprints must be 500 dpi while the size is 300x300

b) Edge Detection

*An edge is the boundary between two regions with relatively distinct gray levelproperties.

*In practice, the set of pixels obtained from the edge detection algorithm seldomcharacterizes a boundary completely because of noise, breaks in the boundary and other effects that introduce spurious intensity discontinuities.

*Thus, edge detection algorithms typically are followed by linking and other boundary detection procedures designed to assemble edge pixels into meaningful boundaries.

c) Thinning

*An important approach to representing the structural shape of a plane region is toreduce it to a graph. This reduction may be accomplished by obtaining the skeleton of theregion via thinning (also called skeletonising) algorithm.The thinning algorithm while deleting unwanted edge points should not:• Remove end points.• Break connectedness• Cause excessive erosion of the region

d) Feature Extraction

Extraction of appropriate features is one of the most important tasks for arecognition system. The feature extraction method used in [1] will be explained below.

*A multilayer perceptron (MLP) of three layers is trained to detect the minutiae in thethinned fingerprint image of size 300x300. The first layer of the network has nine neuronsassociated with the components of the input vector.

*The hidden layer has five neurons and the output layer has one neuron. The network is trained to output a “1” when the input window in centered on a minutiae and a “0” when it is not. Figure 3 shows the initial

*training patterns which are composed of 16 samples of bifurcations in eight differentorientations and 36 samples of non-bifurcations. The networking will be trained using:• The back propagation algorithm with momentum and learning rate of 0.3.• The Al-Alaoui back propagation algorithm. The fingerprint image is scanned using a 3x3 window given

Figure 3. Training set

e) Classifier

*After scanning the entire fingerprint image, the resulting output is a binary imagerevealing the location of minutiae. In order to prevent any falsely reported output and select“significant” minutiae, two more rules are added to enhance the robustness of thealgorithm:

1) At those potential minutiae detected points, we re-examine them by increasing thewindow size by 5x5 and scanning the output image.

2) If two or more minutiae are to close together (few pixels away) we ignore all ofthem.

*The human fingerprint is comprised of various types of ridge patterns, traditionally classified according to the decades-old Henry system: left loop, right loop, arch, whorl, and tented arch.

*Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3, andperhaps 5-10% are arches. Figure 4 shows some fingerprint patterns with the core point is marked.

(a) (b) (c) (d)Figure 4. Core points on different fingerprint patterns. (a) tented arch, (b) right loop, (c) left loop, (d) whorl

*After extracting the location of the minutiae for the prototype fingerprint images, the calculated distances will be stored in the database along with the ID or name of the personto whom each fingerprint belongs.The last phase is the verification phase where testing fingerprint image:

1) is inputted to the system

2) minutiae are extracted

3) Minutiae matching: comparing the distances extracted minutiae to the one stored in the database

4) Identify the personState the results obtained (i.e.: recognition rate).

Second Approach*Most methods for fingerprint identification use minutiae as the fingerprint features.*For small scale fingerprint recognition system, it would not be efficient to undergo all the pre-processing steps (edge detection, smoothing, thinning ..etc), instead Gabor filters will be used to extract features directly from the gray level fingerprint as shown figure 5. No pre-processing stage is needed before extracting the features

ImageAcquisition

FeatureExtractor

Classifier

PhysicalFingerprint

ClassificationDecision

Figure 5. Building blocks for the 2nd approach

a) Image Acquisition

The procedure is the same explained in the 1st approach.

b) Feature Extractor

*Gabor filter based features have been successfully and widely applied to facerecognition, pattern recognition and fingerprint enhancement. The family of 2-D Gabor filters was originally presented by Daugman (1980) as a framework for understanding the orientation and spatial frequency selectivity properties of the filter.

*Daugman mathematically elaborated further his work in [8].In a local neighbourhood the gray levels along the parallel ridges and valleys exhibit some ideal sinusoidal shaped plane waves associated with some noise as shown in figure 6 [3].

Figure 6. Sinusoidal plane wave

The general formula of the Gabor filter is defined by:

*The next step is to specify the values of the filter’s parameters; the frequency is calculated*as the inverse of the distance between two successive ridges. The number of orientation is specified by “m” where

k m k θ =π ( −1) / , k = 1, 2, …., m.

The standard deviations σx and σy are determined empirically. In [7] σx = σy = 2 was used, it is advisable to try other value also.

Figure 7 shows the filter response in spatial and frequency domain for a zero orientation.

Figure 7. Gabor filter response

Table 1 extracted from [8] described the filter properties in space and spectral domains

2D Space Domain 2D Frequency Domain

Table 1. Filter properties

The fingerprint print image will be scanned by a 8x8 window; for each block the magnitudeof the Gabor filter is extracted with different values of m (m = 4 and m = 8). The featuresextracted (new reduced size image) will be used as the input to the classifier.

b) Classifier*The classifier is based on the k-nearest neighbourhood algorithm KNN.

*“Training” of the KNN consists simply of collecting k images per individual as the training set.

* The remaining images consists the testing set. *The classifier finds the k points in the training set that are the closest to x (relative to the Euclidean distance) and assigns x the label shared by the majority of these k nearest neighbours.

*Note that k is a parameter of the classifier; it is typically set to an odd value in order to prevent ties.

*Figure 8 shows how the KNN algorithm works for a two class problem. The KNN querystarts at the test point x and grows a spherical region until it encloses k training samples,and it labels the test point by a majority vote of these samples. In this k = 5 case, the testpoint x would be labeled in the category of the red points

Figure 8. The KNN algorithm

The last phase is the verification phase where the testing fingerprint image:1) is inputted to the system2) magnitude features are extracted3) perform the KNN algorithm4) Identify the personState the recognition rate obtained.

c) Suggested enhancement

*In order to enhance the performance of the 2nd approach below is a list of proposed ideas:

• Instead of using only the magnitude Gabor filter features, try to use also the phaseof the filter [10].• Try to use the Mahalanobis distance given by: D = (x − m)T C−1 (x − m) where m isthe mean and C is the covariance matrix. Appendix A provides an example ofMahalanobis distance.• Try to other classifiers such as back propagation and ALBP. Indicate the number oflayers used as well as the number of neurons.• The Gabor filter assumes a sinusoidal plane wave which is not always the case asdepicted in figure 9. Try to use the modified Gabor filter described in [11].

Figure 9. A fingerprint with corresponding ridges and valleys.

Thank you