30-07-2011, 03:25 PM
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Wavelet Based Palmprint Authentication System
Abstract:
Palmprint is biometric family due to its ease of acquisition, high user acceptance and reliability.
The transforms employed have been analyzed for their individual as well as combined performances at feature level.
The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer.
INTRODUCTION:
Biometrics based personal identification is getting wide acceptance replacing passwords and keys due to its reliability, uniqueness and the ever in-creasing demand of security.
If any biometric is to succeed in the future it should have the traits like uniqueness, accuracy, richness, ease of acquisition, reliability and above all user acceptance.
The Palm-print is rich in information and has been analyzed for discriminating features like where wavelet transform has been used for feature extraction .
Unfortunately, passport, keys, access cards can be lost, duplicated, stolen, or forgotten; and password, secret codes, and personal identification numbers (PINs) can easily be forgotten, Compromised, shared, or observed.
A biometric is a unique, measurable characteristic or trait of a human being for automatically recognizing or verifying identity.
IMAGE ACQUISITION
There are two types of systems available for capturing the palmprint .
Scanners and the pegged systems.
Scanners are hygienically not safe whereas the pegged systems cause considerable inconvenience to the user.
These systems suffer from low user acceptability.
IMAGE REGISTRATION
The acquired color (RGB) parameters of Palmprint are changed to HSI parameters.
Palmprint has been analyzed for its texture using the gray level or intensity values, I among the HSI values.
The gray level images are normalized and thresholded to get a binary image.
The longest line in a palm passes through the middle finger, and any rotation is considered with reference to this line.
FEATURE EXTRACTION AND CLASSIFICATION
The obtained registered palm print image has been analysed for its texture using different symmetrical wavelet families
The palm print region 256x256 has been decom-posed into three scales for each wavelet type.
The selected wavelets have been analyzed for their individual performance by formulating similar energy based feature vectors of length 27, using 9 levels decomposition.
Normalized energy:
The additional energy for performing certain computation-intensive jobs in each system as the energy unit (EU).
The EU for the three systems we studied is between 8 and10µ Joules.
The absolute energy figure for an event varied slightly from day to day.
Concept of palm just like finger:
Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression.
A fingerprint or palm print appears as a series of dark lines and represents the high, peaking portion of the friction ridged skin .
while the valley between these ridges appears as a white space and is the low, shallow portion of the friction ridged skin.
Friction ridges do not always flow continuously throughout a pattern and often result in specific characteristics such as ending ridges or dividing ridges and dots.
A palm recognition system is designed to interpret the flow of the overall ridges to assign a classification and then extract the minutiae detail
Texture analysis:
The image of a wooden surface is not uniform but contains variations of intensities which form certain repeated patterns called visual texture.
A region in an image has a constant texture if a set of local statistics or other local properties of the picture function are constant, slowly varying, or approximately periodic.
WAVELETS Fourier analysis
Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies.
Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost.
The signal properties do not change much over time that is, if it is what is called a stationary signal this drawback isn’t very important.
Short-Time Fourier analysis:
The Fourier transform to analyze only a small section of the signal at a time a technique called windowing the signal.
The STFT represents a sort of compromise between the time- and frequency-based views of a signal.
you can only obtain this information with limited precision, and that precision is determined by the size of the window.
Wavelet Analysis
Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information.
wavelet analysis does not use a time-frequency region, but rather a time-scale region.
What Is Wavelet Analysis?
A wavelet is a waveform of effectively limited duration that has an average value of zero.
Compare wavelets with sine waves, which are the basis of Fourier analysis
Fourier analysis consists of breaking up a signal into sine waves of various frequencies.
It also makes sense that local features can be described better with wavelets that have local extent.
The Discrete Wavelet Transform:
Calculating wavelet coefficients at every possible scale is a fair amount of work, and it generates an awful lot of data.
For many signals, the low-frequency content is the most important part.
The high-frequency content on the other hand imparts flavor.
In wavelet analysis, we often speak of approximations and details.
The approximations are the high-scale, low-frequency components of the signal. The details are the low-scale, high-frequency components.
Since the analysis process is iterative, in theory it can be continued indefinitely.
In reality, the decomposition can proceed only until the individual details consist of a single sample or pixel.
you’ll select a suitable number of levels based on the nature of the signal, or on a suitable criterion such as entropy.
Wavelet Reconstruction:
The discrete wavelet transform can be used to analyze or decompose signals and images. This process is called decomposition or analysis.
The other half of the story is how those components can be assembled back into the original signal without loss of information. This process is called reconstruction, or synthesis.
The mathematical manipulation that effects synthesis is called the inverse discrete wavelet transforms (IDWT).
To synthesize a signal in the Wavelet Toolbox, we reconstruct it from the wavelet coefficients:
Where wavelet analysis involves filtering and down sampling, the wavelet reconstruction process consists of up sampling and filtering.
Up sampling is the process of lengthening a signal component by inserting zeros between samples
Conclusion:
This paper investigates combination of multiple wavelets at feature level for palmprint based authentication system using an indigenously developed peg-free image acquisition platform.
The results depict the superiority of combined wavelets over individual wavelet feature for the palmprint authentication, using coarse level information. The paper also presented a new approach for rotation invariance, which proved its effectiveness by enhancing genuine accep-tance rate.
Wavelet Based Palmprint Authentication System
Abstract:
Palmprint is biometric family due to its ease of acquisition, high user acceptance and reliability.
The transforms employed have been analyzed for their individual as well as combined performances at feature level.
The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer.
INTRODUCTION:
Biometrics based personal identification is getting wide acceptance replacing passwords and keys due to its reliability, uniqueness and the ever in-creasing demand of security.
If any biometric is to succeed in the future it should have the traits like uniqueness, accuracy, richness, ease of acquisition, reliability and above all user acceptance.
The Palm-print is rich in information and has been analyzed for discriminating features like where wavelet transform has been used for feature extraction .
Unfortunately, passport, keys, access cards can be lost, duplicated, stolen, or forgotten; and password, secret codes, and personal identification numbers (PINs) can easily be forgotten, Compromised, shared, or observed.
A biometric is a unique, measurable characteristic or trait of a human being for automatically recognizing or verifying identity.
IMAGE ACQUISITION
There are two types of systems available for capturing the palmprint .
Scanners and the pegged systems.
Scanners are hygienically not safe whereas the pegged systems cause considerable inconvenience to the user.
These systems suffer from low user acceptability.
IMAGE REGISTRATION
The acquired color (RGB) parameters of Palmprint are changed to HSI parameters.
Palmprint has been analyzed for its texture using the gray level or intensity values, I among the HSI values.
The gray level images are normalized and thresholded to get a binary image.
The longest line in a palm passes through the middle finger, and any rotation is considered with reference to this line.
FEATURE EXTRACTION AND CLASSIFICATION
The obtained registered palm print image has been analysed for its texture using different symmetrical wavelet families
The palm print region 256x256 has been decom-posed into three scales for each wavelet type.
The selected wavelets have been analyzed for their individual performance by formulating similar energy based feature vectors of length 27, using 9 levels decomposition.
Normalized energy:
The additional energy for performing certain computation-intensive jobs in each system as the energy unit (EU).
The EU for the three systems we studied is between 8 and10µ Joules.
The absolute energy figure for an event varied slightly from day to day.
Concept of palm just like finger:
Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression.
A fingerprint or palm print appears as a series of dark lines and represents the high, peaking portion of the friction ridged skin .
while the valley between these ridges appears as a white space and is the low, shallow portion of the friction ridged skin.
Friction ridges do not always flow continuously throughout a pattern and often result in specific characteristics such as ending ridges or dividing ridges and dots.
A palm recognition system is designed to interpret the flow of the overall ridges to assign a classification and then extract the minutiae detail
Texture analysis:
The image of a wooden surface is not uniform but contains variations of intensities which form certain repeated patterns called visual texture.
A region in an image has a constant texture if a set of local statistics or other local properties of the picture function are constant, slowly varying, or approximately periodic.
WAVELETS Fourier analysis
Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies.
Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost.
The signal properties do not change much over time that is, if it is what is called a stationary signal this drawback isn’t very important.
Short-Time Fourier analysis:
The Fourier transform to analyze only a small section of the signal at a time a technique called windowing the signal.
The STFT represents a sort of compromise between the time- and frequency-based views of a signal.
you can only obtain this information with limited precision, and that precision is determined by the size of the window.
Wavelet Analysis
Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information.
wavelet analysis does not use a time-frequency region, but rather a time-scale region.
What Is Wavelet Analysis?
A wavelet is a waveform of effectively limited duration that has an average value of zero.
Compare wavelets with sine waves, which are the basis of Fourier analysis
Fourier analysis consists of breaking up a signal into sine waves of various frequencies.
It also makes sense that local features can be described better with wavelets that have local extent.
The Discrete Wavelet Transform:
Calculating wavelet coefficients at every possible scale is a fair amount of work, and it generates an awful lot of data.
For many signals, the low-frequency content is the most important part.
The high-frequency content on the other hand imparts flavor.
In wavelet analysis, we often speak of approximations and details.
The approximations are the high-scale, low-frequency components of the signal. The details are the low-scale, high-frequency components.
Since the analysis process is iterative, in theory it can be continued indefinitely.
In reality, the decomposition can proceed only until the individual details consist of a single sample or pixel.
you’ll select a suitable number of levels based on the nature of the signal, or on a suitable criterion such as entropy.
Wavelet Reconstruction:
The discrete wavelet transform can be used to analyze or decompose signals and images. This process is called decomposition or analysis.
The other half of the story is how those components can be assembled back into the original signal without loss of information. This process is called reconstruction, or synthesis.
The mathematical manipulation that effects synthesis is called the inverse discrete wavelet transforms (IDWT).
To synthesize a signal in the Wavelet Toolbox, we reconstruct it from the wavelet coefficients:
Where wavelet analysis involves filtering and down sampling, the wavelet reconstruction process consists of up sampling and filtering.
Up sampling is the process of lengthening a signal component by inserting zeros between samples
Conclusion:
This paper investigates combination of multiple wavelets at feature level for palmprint based authentication system using an indigenously developed peg-free image acquisition platform.
The results depict the superiority of combined wavelets over individual wavelet feature for the palmprint authentication, using coarse level information. The paper also presented a new approach for rotation invariance, which proved its effectiveness by enhancing genuine accep-tance rate.
