International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015
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Dept. of Electronics & Comm. Engg. Sanghvi Institute of Management & Science, Indore, India
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Asst. Professor & Head, Dept. of Electronics & Comm. Engg., Sanghvi Institute of Management & Science
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Background Abstract :- Now in these access control and authentication mechanism becomes more challenging as the technology is growing rapidly. Meanwhile the bio-matric based access control and authentication take attention by their accurate user access identification. In this presented work the bio-matric authentication techniques are investigated. Thus a detailed survey on exiting and recently available techniques is presented for bio-matric iris or face recognition. In addition of that a new technique for improving the accuracy of identification of iris and face data is proposed. In near future the presented model is implemented using suitable technology and their results are presented.
Keywords — Biometric system, fingerprint recognition, face recognition, iris recognition, DCT,
DWT, FFT, HHT .
I.
Introduction
Biometric Authentication is a practice in which a person's identification and verification information is generated by digitizing measurements of a physiological or behavioral characteristic. Biometric authentication proves user's identity by comparing an encoded value with a saved value of the associated biometric characteristic. Even though, for authentication using biometric techniques is sited well, security and privacy issues like loss or stole of personal bio-information. Moreover, development and improvement of a secure biometrics scheme offers great challenges as bio-information of people is not precisely the same [1].
Authentication is a process which is used in security practice for access control. The common existing methods of authentication, which work during the login stage, are unsecure due to the lack of identification after the initial instance. On the contrary, biometric identification verifies a user‘s identity based on his behavior, both continuously and without interruption. These techniques work by first capturing a sample of the feature, such as a recorded sound for voice recognition, or a captured digital color image for face recognition. The biometric system converted the image or sample of feature into some sort of mathematical function or representations called as biometric template. In the biometric system, biometric template will give a normalized and highly discriminating representation of the feature, which can then be objectively, compared each measurement with another template in order to obtain identity [1].
There are two modes of operations of biometric system. First one is an enrollment mode and another is identification mode. The enrollment mode adds templates to a database, and in the identification mode, a template is created for a one kind of feature and then a match is searched for in the database of pre-enrolled templates [2].
Advantages of using Biometrics
Easier fraud detection
Better than password/PIN or smart cards
No need to memorize passwords
Requires physical presence of the person to be identified
Unique physical or behavioral characteristic
Cannot be borrowed, stolen, or forgotten
Cannot leave it at home
II.
Types of biometric systems
We have a wide variety of different types of biometric systems. Some of them are listed below:
Fingerprint/Palm print: Recognizes the physical structure of a person's fingerprint / palm print, e.g. the minutiae points that include bifurcations and ridge endings.
Hand geometry : Recognizes the shape of a person's hand.
Retina Scan : Patterns of the blood vessels on the back of the eyeball are recognized.
Iris Scan : Recognizes the colored portion of the eye and the each individual patterns, rings in the iris.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015
Signature dynamics: It recognizes the pressure used, slant of a pen, amount of time and different patterns captured in creating a signature.
1.
Fingerprint recognition
Fingerprint recognition and identification refers to the programmed method of verifying a match among two person fingerprints. For the reason of matching, fingerprints recognition used & it generally requires the comparison of many features of the print pattern. The characteristics of fingerprint patterns are ridges, and minutia, which are unique features found within the patterns. Knowledge of the structure and properties of human skin is also important in order to successfully employ some of the imaging technologies.
The fingerprint recognition system provide personal measurement at low cost with high accuracies . Firstly, Fingerprint image is obtained from sensor. And this image is enhanced because enhancement algorithm can improve the clearness of the edge structures of input fingerprint images, then the enhancement image is binarized by fixing the threshold value.
This image is thinned using morphological operations. Then the output image is segmented for details extraction. False minutiae are removed by using Euclidean distance after extraction. After preprocessing, the obtaining data collection and mathematical representation of data collection are matched by using two steps of authentication. [3].
2.
Face recognition
Biometric face recognition, also known as
Automatic Face Recognition (AFR), is one of the particularly attractive biometric approach. This system focuses on the equal identifier that humans use primarily to distinguish one person from another: their ―faces‖. One of its main goals is the understanding of the complex human visual system and the knowledge of how humans represent faces in order to discriminate different identities with high accuracy [2][4].
There are two main stages of face recognition i,e face detection and face recognition. In the first stage, it includes identifying and locating a face in an image., and another stage is recognition stage; it includes feature extraction, where important information for discrimination is saved, and the matching, where the output of recognition is given with the aid of a face database. There are several ways to undergo face detection & recognition. By using Imgage Processing Operations, various classifiers, filters or virtual machines for the former.
Various strategies are being available for Facial
Expression Detection.
3.
Iris scanning
In biometric approaches, the human iris is the most secure technique. In general, an iris recognition algorithm includes four basic steps: image quality assessment, image preprocessing, image feature extraction, and image matching. In general, an iris recognition algorithm includes four basic steps: image quality assessment, image preprocessing, image feature extraction, and image matching. This type of system is the most reliable and accurate Biometric identification system available. Iris Recognition is the identification of an individual based on iris features. In the past few years many methods are used to improve the performance of iris recognition systems. These methods mainly concentrate on the variance, robustness and accuracy of iris recognition systems. Human eyes offer a wide set of biometric qualities that have been considered for personal verification, like retina, sclera and iris.
Structure of human retina can be used in a biometric technology, obtaining very good results.
Eyelashes and eyelids protects iris from external agents, like dust or similar. At the center of iris is located the pupil, that controls the quantity of light passing through eye. Usually, iris is not only characterized by its color (like blue and green or more common like hazel and dark) but even by its pattern, which is usually very complex and rich of details. Therefore it is not shocking that since many years iris patterns have been correctly considered useful for biometric measurement. In eye authentication process, the pupil detection is most vital step to identify the eye. In iris authentication, iris and sclera are used as the prior inputs using to
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015 recognize the eye with various mechanisms like segmentation combining with different versions. The inner edge in the eye is not a normal circle, which may occur problem in accurate recognition [5] [6]
ORL Database:
This is a standard face database also known as AT & T database has been used in the proposed work. AT & T database contains face images of 40 distinct persons. Each person has ten different images, taken at different times, totaling to 400.
Figure shows the 40 individuals in the AT & T Face database. Each face image in the database has a size
112x92 pixel. There are various type of variations in facial expressions. All the images were taken against a dark homogeneous background with the subjects in an up-right, frontal position, with tolerance for some side movements. Though the database has been used in many face recognition researches, it is clear that the number of samples or database size is too small to prove and establish/reason the eventual results. A database with higher size is essential for proving the correctness or accuracy of the face recognition researches. Therefore, a larger database has been developed for this research.
Figure: ORL Database
III.
Feature Extraction Techniques
Discrete Cosine Transform (DCT):
DCT is been mostly used in digital image processing and signal analysis due to its ‗energy compaction‘ property. It compresses most signal information in some coefficients. This scheme is to known as a good candidate for feature extraction and is thus used here as a means of signify the allover variations in the human facial data. The DCT is a series of standard overlapping angular part are taken from normalized set of images and a small subset of coefficients is used to form sub feature vectors.
Unique codes are generated in the sequence of many such sub features, and classification is carried out using a weighted Hamming distance metric [9].
Discrete Fourier Transform (DFT):
Discrete Fourier Transform (DFT) used the extracting the features of an image. The algorithm is fast and has a low complexity rate. The system encodes the features to generate its feature codes.
DFT used in extracting the features of iris image. The algorithm is fast and has a low complexity. The system encodes the features to generate feature codes of iris. Fourier transforms of iris images makes possible to achieve perfect iris recognition with a simple matching algorithm. Experimental evaluation using the CASIA iris image database clearly demonstrates an efficient performance of the iris image. The efficient algorithm of iris recognition using Two dimensional (2D) Discrete Fourier
Transform (DFT) and illustrate how increased iris region improves performance.
Fast Wavelet Transform (FWT):
The Fast Wavelet Transform is a mathematical representation intends to turn a signal in the time domain into a sequence of coefficients based on an orthogonal basis of small finite waves, or wavelets.
The transform can be expanded in the form of multidimensional signals, such as images, where the time domain is change with the space domain. To facilitate the feature extraction, the localized iris is converted from a Cartesian coordinate system to a polar coordinate system. This algorithm uses Fast
Wavelet Transform to extract features of iris. This algorithm is fast and has lower complexity rate
[8][11].
Hilbert Huang Transform (HHT):
The Hilbert–Huang transform (HHT) is an empirically based data-analysis tool. Its basis of development is adaptive, so that it can obtain physically meaningful signification of data from nonlinear and non-stationary processes. Traditional data-analysis methods are all based on linear and
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015 stationary assumptions. Only in recent years have new methods been introduced to determine nonstationary and nonlinear data. Moreover, various nonlinear time-series-analysis methods were designed for nonlinear but stationary and deterministic systems. Analyzing the data from such a system is a daunting problem. Even the generally accepted mathematical paradigm of data expansion in terms of and a previous launched basis would need to be avoided, for the convolution computation priori basis creates more problems than solutions.
IV.
Literature Review
Recently, iris recognition has received more attention for human identification due to its high reliability.
Exploring its origin, W. W. Boles and B. Boashash
[7] presented a new approach for iris detection using wavelet transformation. In this, zero-crossings of the wavelet transform at variety of resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional signals or waveforms are compared with model features using different dissimilarity functions. The algorithm is translation, rotation, and scale invariant. It is also insensitive to variations in the lighting conditions and noise levels.
Further in this domain, Patrick Flandrin and his team [8] presented a modified Empirical Mode
Decomposition (EMD) and utilized it as a filter bank which works identical to the ones in wavelet decomposition.
The DCT calculates a truncated Chebyshev series containing mini-max properties and can be implemented using the Discrete Fourier Transform
(DFT). Using this [C] presented an iris detection scheme which is based on differences of DCT coefficient of overlapped angular patches from iris images. The scheme optimizes the feature extraction capability of the DCT using two databases namely
CASIA database and Bath database. The authors have demonstrated the use of novel patch encoding methods in capturing iris texture information, proposed the worst-case (nearest no match) EER as a new practical metric for evaluating systems, and investigated better classifier designs for wider interclass separability. John Daugman [9] introdued four methods for iris recognition systems. First, the iris inner and outer borders with active contours, leading to more flexible with embedded systems.
Next is about fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and ―rotating‖ the eye into orthographic perspective .
Third one is proposal of statistical methods for detection and exclusion of eye lashes.
Fourth and last one is exploring score normalizations, depending on the amount of iris data that is available in images and the required scale of database search.
Automatic iris recognition based on fractal dimensions of Haar wavelet transformation was presented by Patnala S. R. Chandra Murty et al
[10] . Haar wavelet transforms are used to extract the multiple scale features at different resolutions.Estimation of fractal dimensions from these patterns and classifier are used to recognize the given images from a data base. Naveen Singh et al
[11 ] presented a novel scheme for iris recognition which utilizes a mechanism combining a Canny Edge
Detection scheme and a Circular Hough Transform to recognize iris boundaries in an eye’s boundaries.
Deterministic features in an iris are extracted in the form of a feature vector using Haar wavelet. Finally, hamming distance is used to determine similarity between two iris images.
Jaydeep N. Kale and his team [12] projected an efficient iris recognition scheme which uses 2- dimensional DFT whose phase components determines similarity between two iris images. The algorithm is then tested on CASIA database. V. P. Sharma et al [13] presented an improvement in iris recognition system by using
DWT and feature selection by ant colony optimization method. After extraction of feature of iris used feature optimizations technique for better selection of feature. Detection of corners in the transformed iris image using covariance matrix of change the intensity along with rows and columns.
The selected feature gone through process of pattern generation and finally matched the template value and improved the FAR and FRR of iris image.
Yachna Kumari et al [14] , in their research studied feature extraction using 1-D gabor filter which is used for eyelash detection and removing noise from iris image generated after segmentation and normalization. Noise is estimated in terms of PSNR and MSE.
V.
Proposed Work
Due to its high reliability. recently, iris recognition has received increasing attention for human identification. The document presents a study of different feature extraction methods of iris recognition. From this Hilbert Huang Transform
(HHT) is more efficient than DCT, DFT, and FWT.
HHT is an analysis method for nonlinear and nonstationary data. Furthermore, the paper proposes an efficient iris recognition method based on HHT by extracting the main frequency center information.
This new method is likely to give benefit a lot: firstly, its dimension of feature vector is very low compared with the other famous methods; secondly, other than
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015 most of other iris recognition methods, suggested method need not to enhance processing in the iris image preprocessing and is illumination-invariant; thirdly, unlike most existing methods to achieve approximate rotation invariance by defining several templates denoting other angles, this method is expected to be rotation-invariant; fourthly, it is robust to high frequency noise. This scheme uses CASIA iris database and HHT.
1.
Iris Image Preprocessing:
This step detects the inner boundary and the outer boundary of the iris. Since the localization method in the proposed scheme is a very effective method. The main steps are briefly introduced as follows. Since the pupil is generally darker than its surroundings and its boundary is a distinct edge feature, it can be found by using edge detection (Canny operator in experiments). Then a Hough transform is used to find the center and radius of the pupil. Finally, the outer boundary will be detected by using edge detection and Hough transform again in a certain region determined by the center of the pupil.
2.
Normalization and Enhancement
Due to the dilation and condition of the human pupil, the radial size of the iris varies under various illumination conditions and in response to physiological factors. The resulting deformation of the iris texture can be fairly accurate as a linear deformation. As iris boundaries are already known, one can map a rectangular image array back to an angular and radial position in the iris. This position will not, in general case, map exactly onto a pixel in the source image, so the normalized gray value is produced by bilinear interpolation from its four nearest neighbors. Finally, the gray levels are altered by removing the peak illumination caused by light sources reflecting from the eye; estimating and subtracting the slowly varying background illumination, and equivalent the gray-level histogram of the iris image.
3.
Feature Extraction
The proposed scheme uses HHT feature extraction. It consists of two parts: the empirical mode decomposition (EMD) and the Hilbert spectrum.
With EMD, complicated data set can be decomposed into often small number of intrinsic mode functions
(IMFs). An IMF is defined as a function satisfying the following two conditions: (1) it has exactly one zero-crossing between any two consecutive local extrema; (2) it has zero local mean.
4.
Invariance
It is advantageous to an iris representation invariant to translation, scale, rotation and illumination. In our method, after normalisation step translation invariance and approximate scale invariance are achieved, the original image obtain at the preprocessing step.
5.
Robustness to noise
The high frequency noise is mainly contained in the first IMF. Moreover, it is found that the first IMF is not a significant component to characterize the iris structure. To be robust to high frequency noise, the first IMF is removed when the Hilbert marginal spectrum is calculated in our method. Though it leads a slight change for the main frequency, it can relieve the high frequency noises.
4.
Iris matching
To improve computational efficiency and classification accuracy, Linear Discriminant Analysis
(LDA) is first used to reduce the dimensionality of the feature vector and then the Euclidean similarity measure is adopted for classification. LDA is a linear statistic classification method, which intends to find a linear transform as such that, after its application, the scatter of sample vectors is minimum within each class, and the scatter of those mean vectors around the total mean vector can be maximum simultaneously.
VI.
Conclusion
Biometric authentications systems are widely used in different application areas. This paper incorporates a brief study about different feature extraction schemes and a literature review of different iris recognition schemes being used so far. Further, the document proposes an iris recognition system that uses HHT, which is likely to perform better than existing ones.
References
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International Journal of Engineering Trends and Technology (IJETT) – Volume 26 Number 3- August 2015
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