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ISSN 10546618, Pattern Recognition and Image Analysis, 2010, Vol. 20, No. 3, pp. 360–369. © Pleiades Publishing, Ltd., 2010. 1. INTRODUCTION Fingerprints are biometric signs that can be uti lized for identification and authentication purposes in biometric systems. Among all the biometric indi cators, fingerprints have one of the highest levels of reliability [1]. The main reasons for the popularity of the fingerprintbased identification are the unique ness and permanence of fingerprints. It has been claimed that no two individuals, including identical twins, have the exact same fingerprints. It has also been claimed that the fingerprint of an individual does not change throughout his lifetime, with the exception of a significant injury to the finger that cre ates a permanent scar [2]. Fingerprints are graphical patterns of locally paral lel ridges and valleys with welldefined orientations on the surface of fingertips. Ridges are the lines on the tip of one’s finger. The unique pattern of lines can either be loop, whorl, or arch pattern. Valleys are the spaces or gaps that are on either side of a ridge. The most important features in fingerprints are called the minu tiae, which are usually defined as the ridge endings and the ridge bifurcations. A ridge ending is the point, where a ridge ends abruptly. A ridge bifurcation is the point, where a ridge forks into a branch ridge [3]. Examples of minutiae are shown in Fig. 1. A full fin gerprint normally contains between 50 to 80 minutiae. A partial fingerprint may contain fewer than 20 minu tiae. According to the Federal Bureau of Investiga tions, it suffices to identify a fingerprint by matching 12 minutiae, but it has been reported that in most cases, 8 matched minutiae are enough. Several algorithms have been proposed in the liter ature for fingerprint recognition. Most of these algo rithms are based on extracting geometrical features from the fingerprints and using them for fingerprint matching with available templates. Some of these algorithms take the minutiae and the singular points, including their coordinates and directions, as the dis tinctive features to represent the fingerprint in the matching process [4–7]. Then, the minutiae features are compared with the minutiae templates; if the matching score exceeds a predefined threshold, these two fingerprints can be regarded as belonging to the same finger. These geometrical methods have some limitations such as the difficulty to locate the minutiae correctly and difficulty to work with distorted images. Fingerprint Recognition Using MelFrequency Cepstral Coefficients 1 F. G. Hashad, T. M. Halim, S. M. Diab, B. M. Sallam, and F. E. Abd ElSamie Department of Electronics and Electrical communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt email: [email protected], [email protected] Abstract—This paper presents a new fingerprint recognition method based on melfrequency cepstral coef ficients (MFCCs). In this method, cepstral features are extracted from a group of fingerprint images, which are transformed first to 1D signals by lexicographic ordering. MFCCs and polynomial shape coefficients are extracted from these 1D signals or their transforms to generate a database of features, which can be used to train a neural network. The fingerprint recognition can be performed by extracting features from any new fin gerprint image with the same method used in the training phase. These features are tested with the neural net work. The different domains are tested and compared for efficient feature extraction from the lexicographi cally ordered 1D signals. Experimental results show the success of the proposed cepstral method for finger print recognition at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction. Key words: fingerprint recognition, MFCCs, DCT, DST, DWT. DOI: 10.1134/S1054661810030120 Received September 2, 2009 APPLICATIONS PROBLEMS 1 The article is published in the original. Ridge ending Bifurcation (a) (b) Fig. 1.

Fingerprint recognition using mel-frequency cepstral coefficients

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Page 1: Fingerprint recognition using mel-frequency cepstral coefficients

ISSN 1054�6618, Pattern Recognition and Image Analysis, 2010, Vol. 20, No. 3, pp. 360–369. © Pleiades Publishing, Ltd., 2010.

1. INTRODUCTION

Fingerprints are biometric signs that can be uti�lized for identification and authentication purposesin biometric systems. Among all the biometric indi�cators, fingerprints have one of the highest levels ofreliability [1]. The main reasons for the popularity ofthe fingerprint�based identification are the unique�ness and permanence of fingerprints. It has beenclaimed that no two individuals, including identicaltwins, have the exact same fingerprints. It has alsobeen claimed that the fingerprint of an individualdoes not change throughout his lifetime, with theexception of a significant injury to the finger that cre�ates a permanent scar [2].

Fingerprints are graphical patterns of locally paral�lel ridges and valleys with well�defined orientations onthe surface of fingertips. Ridges are the lines on the tipof one’s finger. The unique pattern of lines can eitherbe loop, whorl, or arch pattern. Valleys are the spacesor gaps that are on either side of a ridge. The mostimportant features in fingerprints are called the minu�tiae, which are usually defined as the ridge endings andthe ridge bifurcations. A ridge ending is the point,where a ridge ends abruptly. A ridge bifurcation is thepoint, where a ridge forks into a branch ridge [3].Examples of minutiae are shown in Fig. 1. A full fin�

gerprint normally contains between 50 to 80 minutiae.A partial fingerprint may contain fewer than 20 minu�tiae. According to the Federal Bureau of Investiga�tions, it suffices to identify a fingerprint by matching12 minutiae, but it has been reported that in mostcases, 8 matched minutiae are enough.

Several algorithms have been proposed in the liter�ature for fingerprint recognition. Most of these algo�rithms are based on extracting geometrical featuresfrom the fingerprints and using them for fingerprintmatching with available templates. Some of thesealgorithms take the minutiae and the singular points,including their coordinates and directions, as the dis�tinctive features to represent the fingerprint in thematching process [4–7]. Then, the minutiae featuresare compared with the minutiae templates; if thematching score exceeds a predefined threshold, thesetwo fingerprints can be regarded as belonging to thesame finger. These geometrical methods have somelimitations such as the difficulty to locate the minutiaecorrectly and difficulty to work with distorted images.

Fingerprint Recognition Using Mel�Frequency Cepstral Coefficients1

F. G. Hashad, T. M. Halim, S. M. Diab, B. M. Sallam, and F. E. Abd El�SamieDepartment of Electronics and Electrical communications, Faculty of Electronic Engineering,

Menoufia University, Menouf, 32952 Egypte�mail: [email protected], [email protected]

Abstract—This paper presents a new fingerprint recognition method based on mel�frequency cepstral coef�ficients (MFCCs). In this method, cepstral features are extracted from a group of fingerprint images, whichare transformed first to 1�D signals by lexicographic ordering. MFCCs and polynomial shape coefficients areextracted from these 1�D signals or their transforms to generate a database of features, which can be used totrain a neural network. The fingerprint recognition can be performed by extracting features from any new fin�gerprint image with the same method used in the training phase. These features are tested with the neural net�work. The different domains are tested and compared for efficient feature extraction from the lexicographi�cally ordered 1�D signals. Experimental results show the success of the proposed cepstral method for finger�print recognition at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosinetransform (DCT) is the most appropriate domain for feature extraction.

Key words: fingerprint recognition, MFCCs, DCT, DST, DWT.

DOI: 10.1134/S1054661810030120

Received September 2, 2009

APPLICATIONSPROBLEMS

1The article is published in the original.

Ridge ending Bifurcation(a) (b)

Fig. 1.

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FINGERPRINT RECOGNITION USING MEL�FREQUENCY CEPSTRAL COEFFICIENTS 361

This paper presents a new cepstral method, whichis not based on geometrical features, for fingerprintpattern recognition. This method is based on generat�ing a database of fingerprint features using the MFCCsand polynomial shape coefficients extracted from dif�ferent fingerprint images with different dimensionsafter they are lexicographically ordered into 1�D sig�nals. A matching process can be performed for anynew fingerprint image to classify it, as belonging to thedatabase or not, using a trained neural network. Thesecoefficients are widely used in speaker identification,because they are robust to noise and insensitive to timeshifts in signals. As a result, there is no need for regis�tration of images, and the extracted features can bevery useful for fingerprint recognition in the presenceof degradations.

The rest of the paper is organized as follows. Sec�tion 2 gives the steps of the proposed fingerprint recog�nition method. Section 3 discusses the process of fea�ture extraction. Feature matching is discussed in Sec�tion 4. In Section 5, the experimental results are given.Finally, Section 6 summarizes the concludingremarks.

2. THE PROPOSED FINGERPRINT PATTERN RECOGNITION METHOD

The proposed fingerprint recognition method hastwo phases; a training phase and a testing phase. In thetraining phase, a database of fingerprint images is usedto extract features from each image. These features areused to train a neural network. In the testing phase,features are extracted from every incoming image anda feature matching process is performed to decidewhether these features belong to a previously knownfingerprint pattern or not. A schematic diagram of thesteps of the proposed detection system is shown inFig. 2.

The steps of the feature extraction process from afingerprint image can be summarized as follows:

(1) The image is lexicographically ordered into a1�D signal.

(2) The obtained 1�D signal can be used in timedomain or in another discrete transform domain. TheDCT, DST, and DWT can be used for this purpose.

(3) MFCCs and polynomial shape coefficients areextracted from either the 1�D signal, the discretetransform of the signal or both of them.

3. FEATURE EXTRACTION

The concept of feature extraction using theMFCCs is widely known in speaker identification [8–17]. It contributes to the goal of identifying speakersbased on the low�level properties. Fingerprint imagesafter lexicographic ordering are treated in this paperlike speech signals. It is clear that the fingerprint hasoscillatory patterns, which supports the application ofthe cepstral method used with speech signals for fea�

ture extraction from these signals. In speaker identifi�cation, the extraction produces sufficient informationfor good speaker discrimination. Experimental resultswill show a great success if the ideas of feature extrac�tion from speech signals are applied to fingerprint 1�Dsignals. Feature extraction can be defined as the pro�cess of reducing the amount of data present in a givenfingerprint signal, while retaining the signal discrimi�native information. In the following subsections, anexplanation for the extraction of the MFCCs and thepolynomial coefficients is presented.

3.1. Extraction of MFCCs

The MFCCs are commonly extracted from signalsthrough cepstral analysis. The input signal is firstframed and windowed, the Fourier transform is thentaken and the magnitude of the resulting spectrum iswarped by the Mel�scale. The log of this spectrum isthen taken and the DCT is applied [8, 9]. Figure 3shows the proposed steps of extraction of MFCCsfrom an image.

The 1�D signal must first be broken up into smallsections; each of N samples. These sections are calledframes and the motivation for this framing process isthe quasi�stationary nature of the 1�D signals. How�ever, if we examine the signal over discrete sections,which are sufficiently short in duration, then thesesections can be considered as stationary and exhibitstable characteristics [8, 9]. To avoid loss of informa�tion, frame overlap is used. Each frame begins at someoffset of L samples with respect to the previous framewhere L ≤ N.

For each frame, a windowing function is usuallyapplied to increase the continuity between adjacentframes. Common windowing functions include therectangular window, the Hamming window, the Black�man window and flattop window.

Windowing in time domain is a pointwise multipli�cation of the frame and the window function. Accord�ing to the convolution theorem, the windowing corre�sponds to a convolution between the short term spec�trum and the window function frequency response. Agood window function has a narrow main lobe and lowside lobe levels in its frequency response. The mostcommonly used window is the Hamming window. TheDFT of a windowed frame of the 1�D signal is com�puted to obtain the magnitude spectrum as follows[8, 9]:

(1)

where x(n) is a time sample of the windowed frame,w(n) is the Hamming window.

The magnitude spectrum |X(k)| is now scaled inboth frequency and magnitude. First, the frequency is

X k( ) w n( )x n( )e j2πkn/N–,

n 0=

N 1–

∑=

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scaled logarithmically using the so�called Mel filterbank H(k, m), and then the logarithm is taken, giving:

(2)

for m = 1, 2, …, M, where M is the number of filterbanks and M � N.

The Mel filter bank is a collection of triangular fil�ters defined by center frequencies calculated on theMel scale [8, 9]. The triangular filters are spread overthe entire frequency range from zero to the Nyquistfrequency. The number of filters is one of the parame�

X ' m( ) X k( ) H k m,( )k 0=

N 1–

∑⎝ ⎠⎜ ⎟⎛ ⎞

ln=

ters which affect the recognition accuracy of the sys�tem. Finally, the MFCCs are obtained by computingthe DCT of X '(m) using [8, 9]:

(3)

for l = 1, 2, …, M, where cl is the lth MFCC. The num�ber of the resulting MFCCs is chosen between 12 and20, since most of the signal information is representedby the first few coefficients. The 0th coefficient repre�sents the average log energy of the frame.

cl X ' m( ) l πM���� m 1

2��–⎝ ⎠

⎛ ⎞⎝ ⎠⎛ ⎞cos

m 1=

M

∑=

Fig. 2. Schematic diagram of the proposed fingerprint recognition method.

Fingerprintimage

MFCCs DCT LnMel�filter bank

and summation forall filter output

Lexicographicordering

Windowing

DFT andcalculation of

magnitudespectrum

Fig. 3. Extraction of MFCCs from an image.

Fingerprintimage

Lexicographicordering

Discretetransform

(DCT, DST, orDWT)

Feature extraction(MFCCs + polynomial

coefficients)

To databaseTraining of a neural

network

Training phase

Lexicographicordering

Discretetransform

(DCT, DST, orDWT)

Feature extraction(MFCCs + polynomial

coefficients)Test image

Identifieldfingerprint or not

Testing phase

Decisionmaking

Feature matching withthe trained neural

network

(a)

(b)

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FINGERPRINT RECOGNITION USING MEL�FREQUENCY CEPSTRAL COEFFICIENTS 363

3.2. Extraction of Polynomial Coefficients

The MFCCs are sensitive to mismatches or timeshifts between training and testing data. As a result,there is a need for other coefficients to be added to theMFCCs to reduce this sensitivity. Polynomial coeffi�cients can be used for this purpose. These coefficientscan help in increasing the similarity between the train�ing and the test signals. If each MFCC is modeled as atime waveform over adjacent frames, polynomial coef�ficients can be used to model the slope and curvatureof this time waveform. Adding these polynomial coef�ficients to the MFCCs vector will be helpful in reduc�ing the sensitivity to any mismatches between thetraining and testing data [15–17].

To calculate the polynomial coefficients, the timewaveforms of the cepstral coefficients are expanded byorthogonal polynomials. The following two orthogo�nal polynomials can be used [15]:

(4)

(5)

To model the shape of the MFCCs time functions,a nine elements window at each MFCC is used. Basedon this window assumption, the polynomial coeffi�cients can be calculated as follows [15]:

(6)

(7)

where al(t) and bl(t) are the slope, and the curvature ofcl in the tth frame. The vectors containing all cl, al, andbl are concatenated to form a single feature vector.

4. FEATURE MATCHING USING ARTIFICIAL NEURAL NETWORKS

The classification step in the proposed detectionmethod is in fact a feature matching process betweenthe features of a new fingerprint image and the featuressaved in the database. Neural Networks are widelyused for feature matching. Multilayer perceptrons(MLPs) consisting of an input layer, one or more hid�den layers and an output layer can be used for this pur�pose [18, 19]. Figure 4 shows an MLP having an inputlayer, a single hidden layer and an output layer. A singleneuron only of the output layer is shown for simplicity.This structure will be used for feature matching,

P1 i( ) i 5,–=

P2 i( ) i2 10i– 55/3.+=

al t( )

P1 i( )cl t i 1–+( )i 1=

9

P12 i( )

i 1=

9

���������������������������������������,=

bl t( )

P2 i( )cl t i 1–+( )i 1=

9

P22 i( )

j 1=

9

���������������������������������������,=

because it is suitable for the problem considered in thispaper.

Each neuron in the neural network is characterizedby an activation function and its bias, and each con�nection between two neurons by a weight factor. In thispaper, the neurons from the input and output layershave linear activation functions and the hidden neu�rons have a sigmoid activation function F(u) = 1/(1 +e–u). Therefore, for an input vector X, the neural net�work output vector Y can be obtained according to thefollowing matrix equation [18, 19]:

(8)

where W1 and W2 are the weight matrices between theinput and the hidden layer and between the hidden andthe output layer, respectively, and B1 and B2 are biasmatrices for the hidden and the output layer, respec�tively.

Training a neural network is accomplished byadjusting its weights using a training algorithm. Thetraining algorithm adapts the weights by attempting tominimize the sum of the squared error between adesired output and the actual output of the outputneurons given by [18, 19]:

(9)

where Do and Yo are the desired and actual outputs ofthe oth output neuron. O is the number of output neu�rons. Each weight in the neural network is adjusted byadding an increment to reduce E as rapidly as possible.The adjustment is carried out over several training iter�ations until a satisfactorily small value of E is obtainedor a given number of epochs is reached. The error

Y W2 ∗ F W1 ∗ X B1+( ) B2,+=

E 12�� Do Yo–( )2

,

o 1=

O

∑=

X1

X2

X3

X1

1

2

3

I

1

2

h

H�1

H

Yo

woh

Inputlayer

Hiddenlayer

Single neuronof the output

layer

whr

Fig. 4. An MLP neutral network.

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back�propagation algorithm can be used for this task[18, 19].

5. EXPERIMENTAL RESULTS

In this section, several experiments are carried out totest the performance of the proposed fingerprint image

recognition method. Time and transform domains areused for feature extraction. The degradations consid�ered are additive white Gaussian noise (AWGN),impulsive noise, and speckle noise with and withoutblurring. These degradations are severe cases, which arerarely studied by researchers in the field of fingerprintrecognition. In the training phase of the proposed rec�

Fig. 5. Samples of the fingerprint images used in the training phase.

100

90

80

70

60

50

40

30

20

10

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10SNR, dB

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 6. Recognition rate vs. the signal to noise ration (SNR) for the different feature extraction methods from fingerprint imagescontaminated by AWGN.

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FINGERPRINT RECOGNITION USING MEL�FREQUENCY CEPSTRAL COEFFICIENTS 365

100

90

80

70

60

50

40

30

20

10

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10Noise variance

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 7. Recognition rate vs. the SNR for the different feature extraction methods from blurred fingerprint images contaminatedby AWGN.

ognition method, a database is first composed. Twentyfingerprint images are used to generate this database.The MFCCs and polynomial coefficients are estimatedto form the feature vectors of the database. In the testing

phase, similar features to that used in the training areextracted from 100 degraded fingerprint images andused for matching. Samples of the fingerprint imagesused in the training phase are shown in Fig. 5.

100

90

80

70

60

50

40

30

20

10

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10SNR, dB

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 8. Recognition rate vs. the percentage error for the different feature extraction methods from fingerprint images contami�nated by impulsive noise.

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The features used in all experiments are 13 MFCCsand 26 polynomial coefficients forming feature vectorsof 39 coefficients for each frame of the image. Sevenmethods for extracting these features are adopted in

the paper. In the first method, the MFCCs and thepolynomial coefficients are extracted from the timedomain signals, only. In the second method, the fea�tures are extracted from the DWT of these signals. In

100

90

80

70

60

50

40

30

20

10

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10Noise variance

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 9. Recognition rate vs. the percentage error for the different feature extraction methods from blurred fingerprint images con�taminated by impulsive noise.

100

90

80

70

60

50

40

30

20

10

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10Noise variance

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 10. Recognition rate vs. the noise variance for the different feature extraction methods from fingerprint images contaminatedby speckle noise.

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the third method, the features are extracted from boththe original signals and the DWT of these signals andconcatenated in single feature vectors. In the fourthmethod, the features are extracted from the DCT ofthe time domain signals. In the fifth method, the fea�tures are extracted from both the original signals andthe DCT of these signals and concatenated in singlefeature vectors. In the sixth method, the features areextracted from the DST of the time domain signals. Inthe last method, the features are extracted from boththe original signals and the DST of these signals andconcatenated in single feature vectors.

A comparison study is held between all theseextraction methods for the above mentioned degra�dation cases, and the results are given in Figs. 6–11.From this comparison, it is clear that the featuresextracted from the DCT of the 1�D signals achievethe highest recognition rates. This is attributed tothe energy compaction property of the DCT, whichenables accurate feature extraction from the firstframes of the 1�D signals after the DCT that cancharacterize each signal. It is also clear that a recog�nition rate of about 100% can be achieved with theproposed method at low degradation cases, unlikethe traditional minutiae based fingerprint recogni�tion techniques.

6. CONCLUSIONS

This paper presented a new cepstral method forfeature extraction from fingerprint image and finger�

print recognition. In this method images are trans�formed to 1�D signals and the MFCCs and polyno�mial coefficients are extracted from the signals. Fea�tures are extracted from the 1�D signals and/or theirtransforms. The proposed method has two phases; atraining phase and a testing phase. A database of thecepstral features of fingerprint images is generated inthe training phase and used for feature matching inthe testing phase. The proposed method is mostlyused in speaker identification, but experimentalresults show that this method can also be used for fea�ture extraction from images. Feature extraction fromthe different transform domains have been tested,and it has also been shown that features extractedfrom the DCT of the 1�D fingerprint signals are themost robust among all other features. This is attrib�uted to the energy compaction property of the DCT,which makes the features extracted from the firstframes after the DCT robust enough to characterizethe signals. Results also show that recognition ratesup to 100% for fingerprints are possible in theabsence of degradations.

REFERENCES

1. W. Chaohong, S. Zhixin, and V. Govindaraju, “Fin�gerprint Image Enhancement Method Using Direc�tional Median Filter,” Proc. SPIE 5404, 66–75(2004).

2. S. Kasaei, M. Deriche, and B. Boashash, “FingerprintFeature Enhancement Using Block�Direction onReconstructed Images,” International Conference on

100

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30

20

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0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10Noise variance

Recognition rate

Features from signalFeatures from the DWT of the signalFeatures from the signal plus DWT of the signalFeatures from DCT of signalFeatures from signal plus DCT of signalFeatures from DCT of signalFeatures from signal plus DCT of signal

Fig. 11. Recognition rate vs. the noise variance for the different feature extraction methods from blurred fingerprint images con�taminated by speckle noise.

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Information, Communications, and Signal Processing,1997, pp. 721–725.

3. L. Hong, Y. Wan, and A. Jain, “Fingerprint ImageEnhancement: Algorithm and Performance Evalua�tion,” IEEE Trans. Pattern Analysis Machine Intelli�gence 20 (8), 777–789 (1998).

4. A. K. Jain, R. Bolle, and S. Pankanti, BIOMETRICS:Personal Identification in Networked Society (Kluwer,New York, 1999).

5. D. Zhang, Automated Biometrics: Technologies and Sys�tems (Kluwer, New York, 2000).

6. K. Hrechak and J. A. McHugh, “Automated Finger�print Recognition Using Structural Matching,” PatternRecognit. 23, 893–904 (1990).

7. A. Jain, H. Lin, and R. Bolle, “On�Line FingerprintVerification,” IEEE Trans. Pattern Anal. Mach. Intell.19 (4), 302–314 (1997).

8. T. Kinnunen, “Spectral Features for Automatic Text�Independent Speaker Recognition,” Licentiate’s The�sis (University of Joensuu, Department of ComputerScience, Finland, 2003).

9. R. Vergin, D. O. Shaughnessy, and A. Farhat, “Gener�alized Mel�Frequency Cepstral Coefficients for Large�Vocabulary Speaker�Independent Continuous�SpeechRecognition,” IEEE Trans. Speech Audio Proc. 7 (5),525–532 (1999).

10. R. Chengalvarayan and L. Deng, “Speech TrajectoryDiscrimination Using the Minimum ClassificationError Learning,” IEEE Trans. Speech Audio Proc. 6(6), 505–515 (1998).

11. P. D. Polur and G. E. Miller, “Experiments with FastFourier Transform, Linear Predictive and CepstralCoefficients in Dysarthric Speech Recognition Algo�rithms Using Hidden Markov Model,” IEEE Trans.Neural Systems Arid Rehabilitation Eng. 13 (4), 558–561 (2005).

12. S. Dharanipragada, U. H. Yapanel, and B. D. Rao,“Robust Feature Extraction for Continuous SpeechRecognition Using the MVDR Spectrum EstimationMethod,” IEEE Trans. Audio, Speech, Language Proc.15 (1), 224–234 (2007).

13. Z. Tufekci, PhD Dissertation (Clemson University,2001).

14. R. Sarikaya, PhD Dissertation (Duke University, 2001).

15. S. Furui, “Cepstral Analysis Technique for AutomaticSpeaker Verification,” IEEE Trans. Acoust., Speech,Signal Proc. ASSP�29 (2), 254–272 (1981).

16. R. Gandhiraj and P. S. Sathidevi, “Auditory�BasedWavelet Packet Filter�Bank for Speech RecognitionUsing Neural Network,” Proceedings of the 15th Inter�national Conference on Advanced Computing and Com�munications, 2007, pp. 666–671.

17. A. Katsamanis, G. Papandreou, and P. Maragos, “FaceActive Appearance Modeling and Speech AcousticInformation to Recover Articulation,” IEEE Trans.Audio, Speech, Language Proc. 17 (3), 411–422(2009).

18. A. I. Galushkin, Neural Networks Theory (Springer�Verlag, Berlin Heidelberg, 2007).

19. G. Dreyfus, Neural Networks Methodology and Applica�tions (Springer�Verlag, Berlin Heidelberg, 2005).

Fatma G. Hashad received theB.Sc. degree from the Faculty ofElectronic Engineering, MenoufiaUniversity, Menouf, Egypt, in 2001.She is currently working towards herM.Sc. degree in electrical communi�cations engineering. Her currentresearch interests are in image pro�cessing.

Salaheldin M. Diab has receivedthe B.Sc. degree from the Faculty ofElectronic Engineering, MenoufiaUniversity, Menouf, Egypt, in 1973,the M.Sc. degree from the Faculty ofEngineering, Helwan University,Cairo, Egypt, in 1981 and the Ph.D.degree from Menoufia University, in1987. He joined the teaching staff ofthe Department of Electronics andElectrical Communications, Facultyof Electronic Engineering. Menoufia

University, Menouf, Egypt since 1987. He has publishedseveral scientific papers in national and international con�ferences and journals. His current research areas of interestinclude adaptive signal processing techniques, superresolu�tion reconstruction of images, speech processing and spreadspectrum communications.

Tadros M. Halim received hisB.Sc. in Communications Engineer�ing from Cairo University, Faculty ofEngineering in June, 1958. From1958 to 1960, he was a full time train�ing engineer in the Egyptian AirForce Training Centre. He receivedhis Ph.D. in the research of deflec�tion defocusing in T.V. Cathode—raytubes from the ElectrotechnicalInstitute of Communications, Mos�cow, VSSR, 1965. He joined the

teaching staff of the Department of Electronics and Electri�cal Communications, Faculty of Electronic Engineering,Menoufia University, Menouf, Egypt since 1966. He haspublished several scientific papers in national and interna�tional conferences and journals. His current research areasof interest include adaptive signal processing techniques,superresolution reconstruction of images, speech process�ing, fingerprint processing, and spread spectrum communi�cations.

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PATTERN RECOGNITION AND IMAGE ANALYSIS Vol. 20 No. 3 2010

FINGERPRINT RECOGNITION USING MEL�FREQUENCY CEPSTRAL COEFFICIENTS 369

Bassiouny M. Sallam has receivedthe B.Sc. degree from the Faculty ofElectronic Engineering, MenoufiaUniversity, Menouf, Egypt, in 1975,the M.Sc. degree from the Faculty ofEngineering, Cairo University,Cairo, Egypt, in 1982 and the Ph.D.degree from Drexel University, USA,in 1989. He joined the teaching staffof the Department of Electronics andElectrical Communications, Facultyof Electronic Engineering, Menoufia

University, Menouf, Egypt since 1989. He has publishedabout forty scientific papers in national and internationalconferences and journals. He has received the most citedpaper award from Digital Signal Processing journal for 2008.His current research areas of interest include adaptive signalprocessing techniques, superresolution reconstruction ofimages, speech processing and spread spectrum communi�cations.

Fathi E. Abd El�Samie receivedthe B.Sc. (Honors), M.Sc., andPh.D. from the Faculty of ElectronicEngineering, Menoufia University,Menouf, Egypt, in 1998, 2001, and2005, respectively. He joined theteaching staff of the Department ofElectronics and Electrical Commu�nications, Faculty of Electronic Engi�neering, Menoufia University, Men�ouf, Egypt, in 2005. He is a co�authorof about 100 papers in national and

international conference proceedings and journals. He hasreceived the most cited paper award from Digital Signal Pro�cessing journal for 2008. His current research areas of inter�est include image enhancement, image restoration, imageinterpolation, superresolution reconstruction of images,data hiding, multimedia communications, medical imageprocessing, optical signal processing, and digital communi�cations.