Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 6 (2014), pp. 571-578 © Research India Publications http://www.ripublication.com/aeee.htm

Analysis of Palmprint Authentication System Using Wavelet Based Multi-resolution Analysis

Mukul Gupta, Manisha Sharma and Dilpreet Tuteja

Electronics & Communication Engineering, Jaypee University of Engineering &

Technology, A.B. Road, Raghogarh, Guna, INDIA.

Abstract A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user. Biometric has gained much attention in the security world recently. Many types of personal identification systems have been developed and palmprint verification is one of the emerging technologies because of its stable, unique characteristics, low-price capture device, fast execution speed also it provides a large area for feature extraction. Palmprint recognizes a person based on the principal lines, wrinkles and ridges on the surface of the palm. Thus palmprint recognition using Wavelet based Multiresolution Analysis is a very interesting research area. A lot of work has already been done in this area, but there is still a lot of scope to make the systems more efficient. Here, we have tried to analyze the already existing systems and thereby propose a new approach. Keywords: Biometrics; Palmprint; Holistic based approach; Wavelet; Multi-Resolution Anaysis.

1. Introduction Each one of the Technologies used in our days bring us a manner to restrict the access to a system, allowing the entrance only to those persons who know a specific code, own a card or have determined physic marks. The more complex is the system, the most difficult is to be attacked, although it will be more expensive and will require more software and hardware resources. When a new authentication system is

Mukul Gupta et al

572

implanted, it is essential to seek a judgment between simplicity, price and efficiency, as well as social acceptability.

The password method is the cheapest and simplest technology, because it only requires elementary software resources. On the other hand, this system is easily attackable, since it is quite simple to obtain the data from a person, either extracting the information to the person itself using deceits, or attacking the software of the system. The Smart Cards are very useful since they can be easily combined with other authentication systems, serving as storage system. But its small size and bend requirements (which are designed to protect the card physically), limits the memory and processing resources. The Digital Signature is very difficult to falsify, since is encrypted by complicated mathematic operations. It is considered that is even less falsifiable than the manual signature recognition (although this last is already enough trustworthy).

The advantage that Biometrics presents is that the information is unique for each individual and that it can identify the individual in spite of variations in the time (it does not matter if the first biometric sample was taken year ago).The pillars of e-learning security are: authentication, privacy (data confidentiality) authorization (access control), data integrity and non-repudiation. Biometric is a technique that can provide all this requirements with quite lot reliability [11].

Biometrics refers to the identification of humans by their characteristics or traits. It is the science of establishing the identity of an individual based on physical or behavioral attributes of the person. One of the most successful biometric systems is the palmprint recognition system [3]. It is reliable due to the fact that the print patterns are always unique, even in the monozygotic twins. The interesting part is that the ridge structure is permanent. This ridge structure is formed at about the thirteenth week of the embryonic development [7]. The palmprint recognition system has advantages over the other physiological biometric systems. Some of the advantages are fixed line structure, low intrusiveness, low cost capturing device, low resolution imaging [6].

Types of Biometrics There are 2 types of biometrics:

1. Behavioral characteristics are related to the pattern of behavior of a person, including typing rhythm, gait, and voice.

2. Physiological characteristics are related to the physical characteristics of the body. Examples include fingerprint, face recognition, Palm print, retina, iris recognition and hand geometry [4].

Applications of Biometric Systems Demand of biometrics is increasing nowadays. It has applications in various fields. The applications of biometrics can be divided into the following three main groups:

1. Commercial applications such as computer network login, electronic data security, ecommerce, Internet access, ATM, credit card, physical access control, cellular phone, medical records management, distance learning, etc.

Analysis of Palmprint Authentication System Using Wavelet Based Multi 573

2. Government applications such as national ID card, correctional facility, driver’s license, social security, welfare-disbursement, border control, passport control, etc.

3. Forensic applications such as corpse identification, criminal investigation, terrorist identification, parenthood determination, missing children, etc [1].

2. Why Holistic-based Approach?

1. In the feature-based approach, the features used are sometimes difficult to extract directly from a given palmprint image with low resolution which is not there in holistic based approach.

2. They are greatly affected by the lighting conditions. 3. The recognition rates and computational efficiency are not strong enough for

palmprint recognition. 4. The holistic-based palmprint recognition uses the original palmprint image as a

whole to extract holistic features. 5. Less computational complexity [8].

3. Why Wavelet Transform? Mathematical transformations are applied to signals to obtain further information from that signal that is not readily available in the raw signal. In the following report we will assume a time-domain signal as a raw signal, and a signal that has been "transformed" by any of the available mathematical transformations as a processed signal [11].

There are numbers of transformations that can be applied, among which the Fourier transforms are probably by far the most popular. Most of the signals in practice are TIME-DOMAIN signals in their raw format. That is, whatever that signal is measuring, is a function of time. In other words, when we plot the signal one of the axes is time (independent variable), and the other (dependent variable) is usually the amplitude. When we plot time domain signals, we obtain a time-amplitude representation of the signal. This representation is not always the best representation of the signal for most signal processing related applications. In many cases, the most distinguished information is hidden in the frequency content of the signal. The frequency SPECTRUM of a signal is basically the frequency components (spectral components) of that signal. The frequency spectrum of a signal shows what frequencies exist in the signal [11].

The transform of a signal is just another form of representing the signal. It does not change the information content present in the signal. The Wavelet Transform provides a time-frequency representation of the signal. It was developed to overcome the short coming of the Short Time Fourier Transform (STFT), which can also be used to analyze non-stationary signals. While STFT gives a constant resolution at all frequencies, the Wavelet Transform uses multi-resolution technique by which different frequencies are analyzed with different resolutions.

Mukul Gupta et al

574

A wave is an oscillating function of time or space and is periodic. In contrast,

wavelets are localized waves. They have their energy concentrated in time or space and are suited to analysis of transient signals. While Fourier Transform and STFT use waves to analyze signals, the Wavelet Transform uses wavelets of finite energy [11].

The wavelet analysis is done similar to the STFT analysis. The signal to be analyzed is multiplied with a wavelet function just as it is multiplied with a window function in STFT, and then the transform is computed for each segment generated. However, unlike STFT, in Wavelet Transform, the width of the wavelet function changes with each spectral component. The Wavelet Transform, at high frequencies, gives good time resolution and poor frequency resolution, while at low frequencies; the Wavelet Transform gives good frequency resolution and poor time resolution [11].

Signal processing application using Wavelet Transform.

4. Multiresolution Analysis Although the time and frequency resolution problems are results of a physical phenomenon (the Heisenberg uncertainty principle) and exist regardless of the transform used, it is possible to analyze any signal by using an alternative approach called the multiresolution analysis (MRA). MRA, as implied by its name, analyzes the signal at different frequencies with different resolutions. Every spectral component is not resolved equally as was the case in the STFT [10].

MRA is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies. This approach makes sense especially when the signal at hand has high frequency components for short durations and low frequency components for long durations. Fortunately, the signals that are encountered in practical applications are often of this type [9].

Analysis of Palmprint Authentication System Using Wavelet Based Multi 575

Multiresolution Image Compression

5. Wavelet Families There are a number of basic functions that can be used as the mother wavelet for Wavelet Transformation. Since the mother wavelet produces all wavelet functions used in the transformation through translation and scaling, it determines the characteristics of the resulting Wavelet Transform. Therefore, the details of the particular application should be taken into account and the appropriate mother wavelet should be chosen in order to use the Wavelet Transform effectively.

(a)

(b)

(c)

(d)

(e)

(f)

Mukul Gupta et al

576

Wavelet families (a) Haar (b) Daubechies4 (c) Coiflet1 (d) Symlet2

(e) Meyer (f) Morlet (g) Mexican Hat. Figure illustrates some of the commonly used wavelet functions. Haar wavelet is

one of the oldest and simplest wavelet. Therefore, any discussion of wavelets starts with the Haar wavelet. Daubechies wavelets are the most popular wavelets. They represent the foundations of wavelet signal processing and are used in numerous applications. These are also called Maxflat wavelets as their frequency responses have maximum flatness at frequencies 0 and π. This is a very desirable property in some applications [2].

The Haar, Daubechies, Symlets and Coiflets are compactly supported orthogonal wavelets. These wavelets along with Meyer wavelets are capable of perfect reconstruction. The Meyer, Morlet and Mexican Hat wavelets are symmetric in shape. The wavelets are chosen based on their shape and their ability to analyze the signal in a particular application.

6. Applications There is a wide range of applications for Wavelet Transforms. They are applied in different fields ranging from signal processing to biometrics, and the list is still growing. One of the prominent applications is in the FBI fingerprint compression standard. Wavelet Transforms are used to compress the fingerprint pictures for storage in their data bank. In DWT, the most prominent information in the signal appears in high amplitudes and the less prominent information appears in very low amplitudes [5]. Data compression can be achieved by discarding these low amplitudes. The wavelet transforms enables high compression ratios with good quality of reconstruction. At present, the application of wavelets for image compression is one the hottest areas of research. Recently, the Wavelet Transforms have been chosen for the JPEG 2000 compression standard.

Wavelets also find application in speech compression, which reduces transmission time in mobile applications. They are used in denoising, edge detection, feature extraction, speech recognition, echo cancellation and others. They are very promising for real time audio and video compression applications. Wavelets also have numerous applications in digital communications. Orthogonal Frequency Division Multiplexing (OFDM) is one of them. Wavelets are used in biomedical imaging. For example, the ECG signals, measured from the heart, are analyzed using wavelets or compressed for

Analysis of Palmprint Authentication System Using Wavelet Based Multi 577

storage. The popularity of Wavelet Transform is growing because of its ability to reduce distortion in the reconstructed signal while retaining all the significant features present in the signal.

7. Conclusion This paper represents the analysis of authentication process using multiresolution analysis with the help of wavelets. Text based password technique is widely used but it is very much unsecured. There are some pros and cons of these techniques. So it is very rarely used where high level of security needed. Token based techniques are widely used at this time but there are some drawbacks of this technique. Earlier biometric techniques were not widely used due to their initial development cost and there maintenance cost, but it is very secure. Now a days, developers are working on the wavelets based palmprint authentication system to overcome the drawbacks of the all above techniques.

8. Acknowledgement The research is partially supported by Jaypee University of Engineering and Technology (JUET), Guna-India. The authors would like to thank Ms. Shefali Sharma, Associate Professor, Electronics & Communication Department from Jaypee University of Engineering & Technology, Guna, India for providing every necessary aid and support for completion of the research.

References

[1] Anil K. Jain, Arun Ross and Salil Prabhakar 2004, “An Introduction to Biometric Recognition” Vol. 14, No. 1.

[2] Murat Ekinci and Murat Aykut 2007, “Palmprint Recognition by Applying Wavelet, Subband Representation and Kernel PCA” P. Perner (Ed.): MLDM 2007, LNAI 4571, pp. 628–642.

[3] Zhang D., Zuo W., and Yue F. 2012, “A comparative study of palmprint recognition algorithms” ACM Comput. Surv. 44, 1, Article 2 (January 2012), 37 pages.

[4] http://www4.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm

[5] Xiang-Qian Wu, Kuan-Quan Wang and David Zhang 2003, “Wavelet Energy Feature Extraction and Matching for Palmprint Recognition” J. Comput. Sci. Trchnol. Vol2.,No. 3, pp. 411-418.

[6] Adams Konga, David Zhang, Mohamed Kamel, “A survey of palmprint recognition”, Pattern Recognition 42 (January, 2009 Elsevier) 1408 – 1418.

[7] Priyanka Somvanshi, Milind Rane, “Survey of Palmprint Recognition”, International Journal of Scientific & Engineering Research Volume 3, Issue 2, February-2012 1 ISSN 2229-5518.

Mukul Gupta et al

578

[8] Chin-Chuan Hana, Hsu-Liang Cheng, Chih-Lung Lin, Kuo-Chin Fan, 2001 “Personal authentication using palm-print features”.

[9] http://biometrics.pbworks.com/w/page/14811357 [10] S. G. Mallat, "A Theory for Multiresolution Signal Decomposition: A Wavelet

Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 11, No. 7, July 1989.

[11] R. Polikar, "The Wavelet Tutorial Part”, http://users.rowan.edu/~polikar/WAVELETS/WTtutorial.html