PALMPRINT AUTHENTICATION SYSTEM

GROUP NUMBER : 16DEPARTMENT : INFORMATION TECHNOLOGYYEAR : 3RD SEMESTER : 6TH

GROUP MEMBERS :ARNAB ROY (48)ISAN KUMAR BOTHRA (10)AWANISH KUMAR (34)

INTRODUCTION :

The widespread penetration of information technology into our daily lives has triggered the real need for reliable and user friendly mechanism to authenticate individuals.

Personal authentication using palmprint has emerged as a promising component of biometric study. While palmprint based authentication approaches have shown promising results, has emerged as one of the most popular and promising biometric modalities for personal identity verification due to its ease of acquisition, non-invasive procedure, high user acceptance and reliability. Efforts are still required to achieve higher performance for their use in high security applications. Prior work on palmprint authentication has shown promising results on inked, scanned, and constrained images, there is great need for better performance in images acquired from unconstrained peg-free setup.

Palmprints have several advantages over other hand-based biometrics, such as palmprint and hand geometry. Compared to palmtips, palms are larger in size and therefore are more robust to injuries and dirt. Also, low-resolution imaging can be employed in the palmprint recognition based on creases and palm lines, making it possible to perform real time preprocessing and feature extraction; and the cost of the capturing device can also be well controlled. Palmprint authentication is believed to be able to achieve the accuracy comparable to that of other hand-based biometric authentication technologies, including palmprint and hand geometry.

One of the possible approaches to achieve higher performance is to integrate palmprint with other biometrics (multimodal systems) or combine various classifiers (intramodal systems) that have shown promising results in palmprint authentication.

In the context of recent work on intramodal biometric systems, palmprint also deserves careful evaluation. Earlier studies have revealed that the palmprint contains mainly three types of information, i.e., texture information, line information, and appearance based information. A generic online palmprint based authentication system considers only texture information while ignoring line- and appearance-based information. Thus the use of single palmprint representation has become the bottleneck in producing high performance. However an ideal palmprint based personal authentication system should be able to reliably discriminate individuals using all of the available information.

Automated personal authentication using biometric features has been widely studied during the last two decades. Previous research efforts have made it possible to apply biometric systems to practical applications for security or commercial purposes. Biometric systems based on palmprint recognition, face recognition, and iris recognition have already been developed to a quite mature stage so that they can be applied to critical security applications such as the immigration control and the crime investigation.

Recently, a novel hand-based biometric feature, palmprint, has attracted an increasing amount of attention. Like any other biometric identifiers, palmprints are believed to have the critical properties of universality, uniqueness, permanence and collectability for personal authentication. Texture and palm lines are the most clearly observable palmprint features in low resolution (such as 100 dpi) images, and thus have attracted most research efforts.

In texture based palmprint authentication approaches, signal processing based texture analysis methods are usually adopted. Typically, texture features are extracted by filtering the palmprint images using filters such as the Gabor filter, the ordinal filter, or the wavelet. The image filtering may be performed in either the spatial domain or the frequency domain. Recently, a lot of automated palmprint authentication methods have focused on the palm line features, since they are more appealing than the texture for the human vision. In the offline method proposed in, the geometric shapes of the palm lines are extracted and approximated by straight-line segments.

The slope, intercept and inclination of each segment are used as features for palmprint matching. C.C. Han et al. investigate the magnitude of palm lines in palmprint matching. The latest related research reveals that the orientations of palm lines also contain strong discriminative power. Based on palm line orientations, a Competitive Code is designed for palmprint representation in; and Y. Han et al. use local orientation histograms for describing palmprints. Similar to the texture based methods; the palm line based methods usually employ image filtering for line feature extraction, leading to a high computational complexity.

For example, in the Competitive Code method, six Gabor filters are applied to each palmprint ROI (Region Of Interest) for generating the corresponding orientation map. Suppose that the Palmprint ROI is 128 x 128 pixels and the Gabor filters are 35 35 in size, the overall MADD (Multiplication + Addition) operations required for one palmprint is around 120 million, leading to a very long processing time especially on slow mobile platforms. Experiments show that extracting the Competitive Code for one palmprint takes more than eight seconds on a state of the art PDA. This is far too slow for a real-time biometric system.

Besides the computational complexity, selecting appropriate filter parameters is also nontrivial in filtering based palmprint authentication methods. It has been demonstrated in that the authentication accuracy varies a lot when using different Gabor filter parameters, which need to be tuned in a try and error manner, indicating that the authentication performance will depend a lot on the training set used for parameter selection.

This may account for the significant performance variations of different filtering based palmprint authentication methods on different databases. In this paper, we propose a texture based approach for palmprint authentication, in which palmprint image grayscale information are directly adopted as features.

The computational complexity of the feature extraction process is much lower than previous filtering based approaches, and thus can be implemented efficiently for even slow mobile embedded platforms. By extending the idea of SAX (Symbolic Aggregate approximation) in time series research to 2D images for palmprint representation and matching, the proposed method can achieve the authentication performance, in terms of EER (Equal Error Rate), comparable to the state of the art palmprint authentication methods. The rest of this paper is organized as the follows. Section 2 explains the 2D extension of SAX for images. Section 3 describes the feature extraction and matching processes of the proposed approach. Experiments and results are elaborated in Section 4. The last section is a conclusion of our work.

DETAILS :

The analysis of palmprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns.It is also necessary to know the structure and properties of humanskinin order to successfully employ some of the imaging technologies.

Patterns :The three basic patterns of palmprint ridges are the arch, loop, and whorl, quite similar to that of fingerprints, however the texture obtained via the lower part palm is and the wrist are a bit complex, as explained later:

Arch: The ridges enter from one side of the palm, rise in the center forming an arc, and then exit the other side of the palm. Loop: The ridges enter from one side of a palm, form a curve, and then exit on that same side. Whorl: Ridges form circularly around a central point on the palm.

1. Arch Pattern 2. Whorl Pattern3. Loop Pattern 4. Full Palm

Scientists have found that family members often share the same general palmprint patterns, leading to the belief that these patterns areinherited.

Minutia features :The majorminutiafeatures of palmprint ridges are ridge ending, bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the palmprint. Minutiae and patterns are very important in the analysis of palmprints since no two palms have been shown to be identical.

Palmprint sensors :

A palmprintsensoris anelectronic deviceused to capture adigital imageof the palmprint pattern. The captured image is called a live scan. This live scan isdigitally processed to create a biometric template (a collection ofextracted features) which is stored and used for matching. This is an overview of some of the more commonly used palmprint sensortechnologies.

Optical :Optical palmprint imaging involves capturing a digital image of the print usingvisible light. This type of sensor is, in essence, a specializeddigital camera. The top layer of the sensor, where the palm is placed, is known as the touch surface. Beneath this layer is a light-emitting phosphor layer which illuminates the surface of the palm. The light reflected from the palm passes through the phosphor layer to an array ofsolid statepixels (acharge-coupled device) which captures a visual image of the palmprint. A scratched or dirty touch surface can cause a bad image of the palmprint. A disadvantage of this type of sensor is the fact that the imaging capabilities are affected by the quality of skin on the palm. For instance, a dirty or marked palm is difficult to image properly. Also, it is possible for an individual to erode the outer layer of skin on the palmtips to the point where the palmprint is no longer visible. It can also be easily fooled by an image of a palmprint if not coupled with a "live palm" detector. However, unlike capacitive sensors, this sensor technology is not susceptible to electrostatic discharge damage. Palmprints can be read from a distance.

Ultrasonic :Ultrasonic sensors make use of the principles ofmedical ultrasonographyin order to create visual images of the palmprint. Unlike optical imaging, ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin. The sound waves are generated usingpiezoelectric transducersand reflected energy is also measured using piezoelectric materials. Since the dermal skin layer exhibits the same characteristic pattern of the palmprint, the reflected wave measurements can be used to form an image of the palmprint. This eliminates the need for clean, undamaged epidermal skin and a clean sensing surface.

Capacitance :Capacitance sensors use principles associated withcapacitancein order to form palmprint images. In this method of imaging, the sensor array pixels each act as one plate of a parallel-platecapacitor, the dermal layer (which is electricallyconductive) acts as the other plate, and the non-conductive epidermal layer acts as adielectric.

Passive capacitance :A passive capacitance sensor use the principle outlined above to form an image of the palmprint patterns on the dermal layer of skin. Each sensor pixel is used to measure the capacitance at that point of the array. The capacitance varies between the ridges and valleys of the palmprint due to the fact that the volume between the dermal layer and sensing element in valleys contains an air gap. Thedielectric constantof the epidermis and the area of the sensing element are known values. The measured capacitance values are then used to distinguish between palmprint ridges and valleys.

Active capacitance :Active capacitance sensors use a charging cycle to apply a voltage to the skin before measurement takes place. The application of voltage charges the effective capacitor. Theelectric fieldbetween the palm and sensor follows the pattern of the ridges in the dermal skin layer. On the discharge cycle, the voltage across the dermal layer and sensing element is compared against a reference voltage in order to calculate the capacitance. The distance values are then calculated mathematically, and used to form an image of the palmprint.Active capacitance sensors measure the ridge patterns of the dermal layer like theultrasonicmethod. Again, this eliminates the need for clean, undamaged epidermal skin and a clean sensing surface.

Algorithms :

Matchingalgorithmsare used to compare previously stored templates of palmprints against candidate palmprints forauthenticationpurposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.

1) Pattern-based (or image-based) algorithms :

Pattern based algorithms compare the basic palmprint patterns (arch, whorl, and loop) between a previously stored template and a candidate palmprint. This requires that the images can be aligned in the same orientation. To do this, the algorithm finds a central point in the palmprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned palmprint image. The candidate palmprint image is graphically compared with the template to determine the degree to which they match.

2) Palmprint authentication using a symbolic representation of images :

The SAX conversion has been widely used in solving data mining problems for 1D time series because it is computationally effi- cient, is easy to use, and is able to achieve a satisfactory balance between dimensionality reduction and discriminative power retaining. In this paper, we propose a natural extension of the SAX representation, 2D SAX, for two-dimensional data such as 2D images. We apply this new representation to the problem of texture based palmprint authentication for testing its effectiveness. Compared to previous palmprint authentication approach, our method mainly has two advantages. Firstly, it is simple to implement and the computational complexity of feature extraction and template matching is much lower than most previous methods, so that it can be efficiently implemented for slow mobile embedded systems. Also, experimental data show that our method is relatively more robust to image blurring. This property is probably useful for mobile palmprint authentication system where images are captured using low resolution/quality embedded cameras and the motion blur are usually inevitable due to the difficulty in user palm fixation. Secondly, our method does not require any training of parameters so that its performance does not rely on the selection of the training dataset and can be easily reproduced. Experimental results show that in term of the authentication accuracy, our method over performs most previous texture based methods, and is comparable to the state of the art palm line bases methods. In the PolyU database, which contains 7752 palmprints, our method can achieve an EER of 0.3% for a one to one verification experiment, and a rank one identification accuracy of 99.90%. On the more challenging CASIA database, an EER of 0.9% can be achieved. It should be emphasized that our method does not impose any assumptions on the palmprint texture and only the grayscale information is used. This indicates that our method can actually be applied to any other texture based image recognition problems. In addition, our method provides a lot of flexibility for practical applications. By choosing different values for SAX_Length and SAX Level, different level of dimensionality and numerosity reduction can be achieved. Introducing the filtering bank G also provides the possibility of utilizing more complex texture features. This might be a possible way for further improving the accuracy of our method. Of course, as a trade off, the computational complexity will usually increase when more complicated filters are used. For example, if the G is a Gabor filter and SAX Level is set to 2, the generated palmprint template in our method is identical to the PalmCode. What is more, it is quite probable that some effective 1D SAX data mining techniques [22], such as motif finding, indexing, abnormity locating and classification, can also be extended to 2D SAX, leading to many potential applications in image data mining. All these are worthy of our future efforts.

3) Personal authentication using multi-feature technique :

Recently, biometric features have been widely used in many personal authentication applications. Biometrics-based authentication is a verification approach using the biological features inherent in each individual. Thus, many access control systems adopt biometric features to replace the digit-based password. In this paper, we propose a scanner-based personal authentication system using the palm-print features. It is very suitable in many network-based applications. The authentication system consists of enrollment and verification stages. In the enrollment stage, M hand images of an individual are collected as the training samples. These samples should be processed by the pre-processing, feature extraction, and modeling modules to generate the matching templates. In the verification stage, a query sample is also processed by the pre-processing and feature extraction modules, and is then matched with the templates to decide. whether it is a genuine sample or not. In our proposed palm-print-based identification system, the pre-processing module, including image-thresholding, border-tracing, wavelet-based segmentation, and ROI location steps, should be performed to obtain a square region in a palm table which is called ROI. Then, we perform the feature extraction process to obtain the feature vectors by the Sobel and morphological operations. The reference templates for a special user are generated in the modeling module. In the verification stage, we use template matching and BPNN to measure the similarity between the reference templates and test samples. In our experiments, the samples are verified by the template-matching and BP neural-network algorithms. In the crest experiment, three kinds of window sizes 32 32, 16 16, and 8 8 are adopted to evaluate the performance of the template-matching methodology. The multiple template-matching algorithm can achieve the accuracy rates above 91%. Both FAR and FRR values are below 9%. Next, the BPNN architecture is adopted to decide whether the query sample is a genuine or not. In this experiment, the average accuracy rates are above 98% for both Sobels and morphological features. Besides, both FAR and FRR values are below 2%. Experimental results verify the validity of our proposed approaches in personal authentication.

CONCLUSION :

In this paper Palmprint recognition algorithms are reviewed. Palmprint recognition has considerable potential as a personal identification technique as it shares most of the discriminative features with fingerprints and in addition possesses a much larger skin area and other discriminative features such as principal lines, ridges and wrinkles which are very useful in biometric security. Coding based techniques have proven to be efficient in terms of memory requirement and matching speed. Fusion technique is recent area in which researchers used to fuse features like appearance-based, line and texture features from palm-prints, which has led to an increase in accuracy. Recent work involves use of multiscale, multi-resolution based techniques like wavelets and contourlets are for efficient implementation of palm print recognition.