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Friday, 14 March 2014

seminar on 3D SCANNER SYSTEM



3D SCANNER SYSTEM
INTRODUCTION
We live in a three-dimensional world. Yet the images we see in magazines and on websites are just two-dimensional. They literally give us only part of the picture. Flatbed scanners are commonplace in many home and office environments, representing the third largest segment of the PC peripherals market. But they can presently only work in two dimensions. Flat objects such as photos, house plans, and letters are 'scanned' and displayed as a two-dimensional digital image file.
Creating 3D models manually is time consuming and creates a bottleneck for many practical applications. It is both difficult to model complex shapes and to recreate complex object appearance using standard parametric reflectance models. Not surprisingly, techniques to create 3D models automatically by scanning real objects have greatly increased in significance. An ideal system would acquire an object automatically and construct a detailed shape and appearance model sufficient to place the synthetic object in an arbitrary environment with new illumination.
As a part of our B.E. syllabus for project work, we are working on developing a laser based 3D scanner i.e. a scanner that uses laser to scan 3D objects and creates an exact view of the scanned object. This presentation will consist of two sections; an explanation of the design of a laser based 3D scanner, followed by a description of applications that make use of such technology. An insight into the design of a laser based 3D scanner system will be presented, from the initial concept through to scanned 3D image. The first part focuses on the construction and working of the 3D scanner. The second part of the presentation will focus on applications possible today using such technology. Finally, advantages, limitations and future developments of the laser based 3D scanner will be discussed.
The laser based 3D scanner is capable of generating a three-dimensional image, but one that doesn't require special glasses or other equipment to view. Displaying jewellery and other small products for sale over the Internet or managing museum collections of plant or animal specimens could be revolutionized by the laser based 3D scanner for scanning and displaying 3D objects.

The 3D scanner will allow users to scan small objects and obtain a digital image file, which is three-dimensional, and so gives extra information about the object's surface hape and texture. The file could be emailed to a friend, put on a website, published in a print catalogue or displayed in an art gallery.
SCOPE OF SYSTEM

Comparison of laser based scanners with electromechanical scanners
Electromechanical scanner has an electromechanical arm that moves along the surface of the object in x and then in y direction and gets the all the co-ordinates of the 3D object and reconstructs the object using these co-ordinates to give the exact 3D view of this object.

Differences between laser based scanners and electromechanical scanners
§  Laser based scanners can precisely scan soft objects where as, electromechanical scanners are not precise to scan soft object as they change the shape of the object while scanning it.
§  Laser based scanners are lighter than electromechanical scanners.
§  Laser based scanners are safer than electromechanical scanners as they do not come in contact with the object to be scanned and so cannot cause any harm to the object.
§  Laser based scanners are cheaper as compared to the electromechanical scanners.
§  Scanning concave surfaces is difficult with laser based scanners where as, electromechanical scanners can do it precisely.

LITERATURE REVIEW
Keywords

§  Laser based 3D scanner
§  Vibrator
§  Laser profiles
§  Profile extraction
§  Wire mesh
§  Navigation tool
§  Surface rendering
§  Electromechanical scanner

Requirements for construction of the 3D scanner

Hardware requirements
The hardware components required are rotating platform, laser gun, vibrator, video camera and a stepper motor. The object to be scanned is placed on a rotating platform. The platform is rotated using a stepper motor, if necessary. A laser gun is used to create a laser profile on the object to be scanned. A vibrator vibrates this laser gun which forms a vertical plane of the laser on the object to be scanned. A video camera is used to capture the rotating object along with the laser profile on it. This method results in an uncomplicated hardware design that is both affordable and reliable.

Software Requirements
The software components required are as follows:
§  Microsoft Visual Basics 6.0
  • Video Edit software utility
  • Graphics library
  • Microsoft Access
  • Navigation Tool
Construction of vibrator
A laser beam is to be used to scan the 3D object. The laser beam is vibrated using a vibrator circuit to form a vertical plane. The vibrator circuit is constructed using a 555-timer circuit, which gives a frequency of approximately 10Hz. But, we require a higher frequency as our video camera captures frames at the rate of 25 frames/sec and in order to obtain the accurate curve of the profile we need at least 20 points in each frame. Thus the desired frequency is greater than or equal to 20 points X 25 frames = 500Hz. So an amplifier is used to amplify the frequency obtained as the output of the timer circuit. Output of the amplifier i.e. high frequency is applied to a 6-volt relay.

When a 9-volt power supply is applied to the circuit, the coil of the relay gets electromagnetically induced. Due the magnetic field generated by the coil, the metallic flap in front of the coil starts vibrating with the desired frequency. As the laser gun is too heavy to be vibrated, a thin mirror of very small size is vibrated on which the laser is projected to form the laser beam. The mirror to be vibrated is mounted on the metallic flap of the relay. Thus, the vibrator forms a vertical plane of the laser of the required frequency.

Applications
Because 3D modeling using laser scanning is both fast and cost-effective, it is well suited for many applications. The potential applications of this device look very interesting.


  Visualization of the photo realistic appearance
One of the persistent problems in 3D graphics today is how to automatically generate photo realistic 3D content that can be put on the web or used in movies, broadcast media, and computer games. Most of the existing 3D content providers sell 3D models that only describe the object's shape, because the object's appearance is too difficult to acquire, process, and display. The 3D scanner technology allows for simple acquisition, description, and fast visualization of the photo realistic appearance of objects.

  Educational Application
Electronic educational material providers are able to enhance their products, such as e-books or electronic encyclopedias, with realistic, interactive 3D illustrations that better explain new concepts and ideas.

  E-commerce
E-commerce generally takes on a new look; companies are able to make their online presence more appealing by placing natural looking 3D models of their products on their websites.

 Mechanical Engineering

Laser scanning is ideal for mechanical applications because the software solutions available convert point data into CAD primitives quickly and accurately. Laser scanning also opens the door for many firms that initially prefer to sculpt objects in traditional mediums to retain the tactile and visual advantages that CAD systems lack.


§  Construction Design
Construction design is one of the largest areas for 3D modeling development. Applications include roadway, bridge, and building design and rehabilitation. Designing construction projects using 3D modeling has been found to have many benefits such as efficient generation of multiple views, minimization of coordination issues with virtual design and construction.

§  Industrial Design
For rapid prototyping, reverse engineering, CAD/CAM, 3-D modeling.

§  3-D Game Software Development
Quickly and easily scan and digitize character models.

§  Animation & Virtual Reality

The market that offers a huge potential is the animation market. That could be animation in movies or animation in computer games. This is such a fast moving industry that software houses are always striving to find ways to make their games more and more realistic. Guiding yourself down a ski slope or through a war zone is probably about as realistic as it can get without actually doing it!


§  Research
For analyzing 3-D data fields like human interfacing and robotic visioning, FEA and mold flow analysis.

§  Product Prototyping
Prototype of the product can be made and approved, scanned, and a mould can be made of any proportion quickly and easily; all of this happening in a matter of days and also reducing the total cost. Thus, the desired product can be sculpted easily and then scanned to insure the intended result.

§  Internet shopping

Laser scanning to scan human body can be used to enhance Internet shopping sites in a number of ways. First of all from a visualization point of view, it would be preferable for a customer to see them selves dressed in the clothing to see what they look like from all angles before making a purchasing decision. This application exists today in the form of aviators, but it won't be long before you can actually dress yourself instead of a virtual mannequin. Additionally, by having the body scan of a customer the Internet retailer knows their exact measurements so that clothing can be supplied in the correct size first time.






§  Automobile Design
Designing a car in AutoCAD is difficult as exact shape and curves of the car cannot be obtained so a clay model of the car can be scanned using 3D scanner.

§  Design collaboration from remote location
In today's world, manufacturing processes are carried out by multiple parties, often from different locations around the globe. The client and the design process can be in one place, while the manufacturing occurs in another. The synergistic effect of having several people collaborating on the development of an idea substantially broadens the scope of the design and manufacturing process. Once a prototype has been scanned, the engineering, analysis, quality control, and various other functions that used to take place consecutively, can take place concurrently before committing to manufacturing. All parties involved with the project can work from the same digital file. The result is a shortened development cycle, improved product performance, and greater flexibility -- positive ramifications at every level.

§  Medicine
If doctor is not available for an operation, the patient’s body can be scanned using 3d scanner and the scanned image can be sent to the doctor. Virtual operation theatre can be created where the doctor carries out the operation and gives command to carry out the operation similarly on the patient.

Advantages
§  One of the great advantages of the 3D scanning technology is that only the laser light makes contact with the surface allowing even very vulnerable surfaces to be scanned without risk. The lasers used in laser scanning are the same type of low-power red light laser used in barcode readers and CD players and are not capable of generating enough light emission to cause damage.

§  Laser scanning is precise even with soft objects.

§  One of the most obvious benefits to 3D scanning is the tremendous increase in speed with which a prototype can be reproduced.

§  Product verification is another advantage of 3D scanning. After a product has been produced, it can be scanned and the resulting data compared to the CAD drawing. Deviations from the specifications can then be accurately determined. This allows for greatly improved quality control, and helps to detect flaws in the manufacturing process.

§  As the object to be scanned is never touched physically, it is not harmed in any way.


IMPLEMENTATION
Working principle of the 3D scanner
Following are the steps in the operation the 3D scanner:

Hardware Setup
The 3D scanner shines a safe, low-intensity laser on an object to create a lighted profile. A high-quality video sensor placed at a pre-calculated angle with respect to the laser captures this profile. The angle is selected so that there is sufficient relief and the hidden areas can be efficiently managed. At 0° the camera could not detect the relief, at 90° the relief would be optimum, but the slightest variation would prevent camera from detecting the laser trace.

 The system can digitize thousands of these profiles in a few seconds to capture the shape of the entire object.

Scanning the object
The object to be scanned is placed on the rotating platform. The laser beam vibrates to form a vertical plane, which falls on the object to trace a curve on the object. When initially projected, the lines are straight and vertical, but upon hitting a curved surface (e.g. a human body) the lines distort and bend. This is the principle behind the laser based 3D scanner. By analyzing and comparing a series of such patterns, it is possible to build up an accurate 3D image of the surface. The video camera captures the object along with the laser profile on the object. The object keeps on rotating and the camera captures the entire scene of complete rotation of the object with all laser profiles. The obtained video is in AVI (Audio Video Interleave) format. A software utility called ‘Video Edit’ converts this video into frames i.e. BMPs. A desired number of frames are selected from all the frames. Let’s say we want 18 vertical profiles i.e. one profile at every 20 degrees and considering the time to scan the entire object to be 18 seconds, then we want one frame after every 1 second and our video camera captures frames at the rate of 25 frames/second. So, we want every 25th frame i.e. if we select 1st frame then the next frames to be selected would be 26,51,76,…..401,426. All these 18 frames would be saved and the rest discarded.

Profile extraction, point identification and storage into database
To understand color scanning, we must first understand what it is we are trying to capture. What is color and how is it interpreted by the camera and the human eye? As we know, white light is made up from equal parts of red, green and blue light, which gives its ‘RGB’ value. Surfaces appear to have different colors because they reflect or absorb certain parts of the white light. For example a perfectly red surface appears to be red when a pure white light is shone on it because it absorbs all of the blue and green and reflects only the red component.
Other colors are produced from a combination of different levels of red, green and blue. The two extremes are black and white surfaces where the black absorbs all colors and reflects none, whereas the white reflects all colors and absorbs none. Humans have the ability to interpret colors because at the back of the eye are cells that react to the different red, green and blue components of light. The brain then translates the level of reaction from each of the cells to produce what we understand to be color. Digital cameras operate using a similar principal except instead of biological cells, there are CCD cells that produce an electronic signals are processed by a computer before being displayed on a screen.
Now all the selected frames are scanned one at a time to extract the laser profile from the frame. Each frame is scanned for its ‘RGB” values. Using the above principle, the point at which ‘R’ value is maximum will be the point on the laser profile. The corresponding x and y values of that point will be stored in the database. But if we consider the thickness of the laser line then we will see that for same y value there are 4 to 5 x values i.e. one laser point covers 4 to 5 pixels. Now two methods can be used to take only one x value for corresponding y value.
 Either consider the first pixel and leave all other pixels with same y values and store this x and y values in the database or take the mean of x values of all the pixels with same y values and then store the x and y values in the database.


Here we store first x value for a corresponding y value and the y value itself in the database. Thus we save all x and y values for all the curves in the database.

Construction of wire mesh diagram
The database contains x and y values of all the extracted laser profiles in matrix form where each column gives x and y values of all points on one curve. So to construct the wire mesh diagram we connect the points in the database.


On connecting the points in the database column wise we get the longitudinal curves and on connecting the points row wise we get latitudinal curves. Thus we get the wire mesh diagram of the scanned object.
Viewing and hidden surface removal
A navigation tool is created which allows us to view the wire mesh diagram from any direction. Graphical transformations like translation, scaling and rotation can also be applied to the wire mesh diagram. As we view the object from any one direction the backside of the object is not visible. So to make the back portion of the object invisible we use algorithm for hidden surface removal. In the hidden surface removal algorithm, small polygons formed by the wire mesh diagram are scanned in anticlockwise direction to get normal to the polygon. If the normal points to the viewer i.e. dot product of the normal with the viewers direction is greater than zero then the polygon is visible and it is drawn else if the dot product is zero or less than zero than the polygon is made invisible i.e. it is not drawn. This algorithm is applied to all the polygons of the wire mesh diagram.

Surface rendering
Surface rendering is done to apply surface and colors to the wire mesh diagram. As color information in 3D digitizing makes available nearly all the information a graphics application needs to fully describe an object. In addition to enhancing realism in graphic models, color denotes boundaries that are not obvious from shape alone. Color indicates surface texture and reflectance. And by marking an object's surface before digitizing, one can use color to transfer ideas from the object to the graphic model. In specialized applications, color can reveal characteristics such as skin discoloration, the locations of landmarks, or other features. Working in the infrared region, a customized color subsystem could even detect surface temperature.
Some of the rendering techniques are scalar graphs, is surfaces, cutting planes, orthogonal slices, vector glyphs, streamlines, streak lines & particle advection and textures which give the exact view of the scanned object. We are not sure which of these techniques we would be using, but if time permits we will be creating our own rendering software.





FUTURE SCOPE AND CONCLUSION

 

Future developments

The public has very high expectation levels that are set by exposure to television, computer games and digital photography. If 3D internet applications are to be accepted by the public then the images have to be as real as possible in terms of both geometry and color. For virtual try-on, the person on the screen has to look like the subject otherwise the application will not be adopted and will eventually fail. Recent advances in lighting positions and techniques improvements in surface quality and integrity are bringing us even closer to this goal. One day using a 3D colour image of your self will be as common place as using a digital camera is today. Trying on clothes over the internet or appearing as yourself in a computer game will be the norm. Improving the quality of the images to meet the public’s expectation levels is a key factor in making this possible.
Transparent and translucent objects have multiple reflections which makes it difficult to classify the points. So, in future we plan to work on solving this problem so that these objects can also be scanned.

Conclusion
Quantifying physical abnormalities, guiding corrective and plastic surgery, manufacturing clothing, three-dimensional CAD and other related fields all benefit from the increasing use of 3D scanners. 3D scanners can give a very precise reconstruction of the shape of a real object
This laser based 3D scanner is easy to use and enables us to scan opaque objects accurately. Its major advantage is that it is less expensive as compared to other 3D scanners available. As it is light-weight and smaller in size it can be easily carried to any place. Laser based 3D scanning can provide a measurable difference for improved quality and accelerated time to market, while reducing costs for new products.
Our experience of working on this project is good as the project is very interesting and challenging. The difficulties encountered during the development of the scanner helped us in learning in many new concepts which we could use to improve the scanner.
We are working to make this scanner more efficient and reliable. 
REFERENCE

§  Mastering Visual Basics 6  -  Evangelos Petroutsos

§  Computer Graphics  -  Steven Harrington

§  Advanced Electronics  -  Ramakant Gaikwad

§  For information on Laser Based 3D Scanner
http:://www.3dscanner.co.uk/
http:://www.muellerr.ch/engineering/laserscanner/default.htm
http:://www.mwm.com/guide/image/analysis.htm
http:://biocomp.stanford.edu/3dreconstruction

seminar on Dynamic Signature Verification System




Dynamic Signature Verification System

ABSTRACT
In this paper we propose a client/server based dynamic signature verification systems for the applications requiring user authentication on the WWW. Both client and server side models are proposed. The signature database and the matching process is implemented through the incremental training algorithm and recall process of neural network based Recognition Engine. Client extracts the dynamic features such as changes in speed, pressure and timing that occur during the act of signing locally. The pattern is an encrypted by client software for security and sent to the server for subsequent processing (i.e. tanning/recall).






















Contents

Title                                                                                                    Page No.

           Abstract                                                                                    
1         Introduction                                                                                       1
2        Dynamic signature verification System                                         3
3         Signature Technology                                                                     5                     
4        Proposed client-server model                                                          7

5          Feature Extraction                                                                           9         
6         Comparison Process                                                                       10
7         Advantages of DSVS                                                                       11
           
8          Applications of DSVS                                                                     11

9          DSVS:Strength and Weakness                                                    12

10      Conclusion                                                                                         13       
                                                 
            References                                                                                       14       





















A
PAPER
ON

“DYNAMIC SIGNATURE VERIFICATION SYSTEM USING  NEURAL NETWORK”



1. Introduction
Signature verification is a process used to recognize an individual's handwritten signature. Dynamic signature verification technology uses the behavioral biometrics of the handwritten signature to confirm the identity of a computer user. This is done by analyzing the shape, speed, stroke, pen pressure and timing information during the act of signing. Natural and intuitive, the technology is easy to explain and trust. As a replacement for a password or a PIN number, dynamic signature verification is a biometric technology that is used to positively identify a person from their handwritten signature. There is an important distinction between simple signature comparisons and dynamic signature verification. Both can be computerized, but a simple comparison only takes into account what the signature looks like. Dynamic
Signature verification takes into account how the signature was made. With dynamic signature verification it if not the shape or the look of the signature that is meaningful; it if the changes in speed, pressure and timing that occurred during the act of signing. Only the original signer can recreate the changes in timing and X, Y and Z (pressure). A pasted bitmap, a copy machine or an expert forger maybe able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes have in X, Y, Z (pressure). The practiced and natural motion of the original signer would require repeating the pattern shown. There will always be slight variation in a person's handwritten signature, but the consistency created by natural motion and practice over time creates a recognizable pattern that makes the handwritten signature a natural for
biometric identification. Signature verification is natural and intuitive. The technology easy to explain and trust. The primary advantage that signature verification system has over other types of biometric technologies is that signatures are already accepted as the common method of identity verification. This history of trust means that people
are very willing to accept a signature based verification system. Dynamic signature verification technology uses the behavioral biometrics of the handwritten signature to confirm the identity of a computer user. Unlike the older technologies of password and key cards - which are often shared or easily forgotten, lost and stolen – Dynamic signature verification provides simple and natural methods for increased
computer security and trusted document authorization.


2. Dynamic Signature Verification System(DSVS)



DSVS brings handwritten signatures to the information age. The technology uses the behavioral biometrics of a handwritten signature to confirm the identity of a computer user. Enterprise-wide and over the internet-unlike a password, PIN number or keycards which are often shared and could be easily lost ,stolen or forgotten, DSVS
provides increased security and trusted document authorization. DSVS may be used to create application for E-commerce, and this application could include non-refutable documents personally signed electronic documents that can be trusted as temper- proof. DSVS offers a secure and natural solution-today-for private communication, data privacy, access control, document authorization, online shopping, electronic payments, member registration, online banking and more.


DSVS may be implemented as technology that requires integration into a client or server application. Software for client or server communication can be provided and at each client side a digitizing tablet is required. The user provides sample signatures at start up enrollment, creating a biometric template that is sorted in a commercially
available database and on a secured DSVS server. During use, to confirm user’s identity, the encrypted data from a subsequently written signature is compared, at the server, with the secured template. The DSVS server users a powerful recognition engine based on neural network.
ÿ An incremental learning algorithm may be used to take of such deviation in signature patterns over time. Some of the features used are shown in figure 2.2.the technology incorporates a learning function, which automatically absorbs and
reflects natural changes in a signature over time.
ÿ Recognition engine uses the timing of changes in pressure, shape, direction, speed and velocity in the Dynamic signature verification Process, while all other verification
algorithms ignored pressures. Biometric identity data is stored in a secured location and is not transported. Less sophisticated architecture may require that the individual's identity data be transported and stored with each copy of every document or transaction, and that the verification is done at the client side. When the recommended
DSVS client/server architecture is utilized the biometric data is secure. With DSVS the identity data is stored, and verification occurs only on a secure server.
ÿ DSVS offers a real-time one-toone security solution without passwords E for government, legal, medical, banking, access control, Data privacy, personalized document approval, online shopping and more. DSVS is the electronic signature for today's networked and online world.
3. Signature Technology


Signature identification systems analyze two different areas of an individual's signature: the specific features of the signature and specific features of the process of signing one's signature. Features that are taken into account and measured include speed, pen pressure, directions, stroke length, and the point in the time when the pen is lifted from the paper. Signature identification devices also can analyze the" static" image of one's signature. In using the “static” image method, the signature identification device captures the image of one's signature and saves it for feature comparisons to the stored template.



To account for the change in one's signature over time, signature identification  systems adopt to any slight variances over time. The way dynamic signature identification system accomplices revealed is by recording the time, history or pressure, velocity, location and acceleration of the pen each time a person uses the system.


4. Proposed Client -- Server Model
The proposed client and server models are shown in figure 4.1 and figure 4.2 respectively. The client extracts the dynamic features such as changes in speed, pressure and timing that occurred during the act of signing locally. The pattern is then encrypted by client software for security and sent to the server for subsequent processing (i.e. Training/Recall).
The design of signature verification systems requires solutions to five types of problems
      
A. Data acquisition
D. Comparison process
B. Preprocessing
E. Performance evaluation
C. Feature extraction
A static signature verification system receives a 2D image as input from the Camera or scanners. Such a system requires a lot of memory and computing power to process the images. The major algorithmic challenge is the required invariance to the current disposition of the writer: no two signatures are fully identical, even after transformation. A dynamic signature verification system gets its input from a digitizer or other, usually pen based, dynamic input device. The signature is then represented as one or several time-varying signals. In other words, the verification system focuses
on how the signatures being written rather than how the signature was written. This provides a better means to grasp the individuality of the writer but fails to recognize the writing itself. Intuitively, this must be correct, being fully in line with science fiction literature: “the irregularity of the hammer blows used by each artisan followed characteristic patterns to an extent that the maker can be identified without question by sampling that pattern. Collectors developed the method to verify authentically. It’s as definite as an eye print, more positive than any skin-print anomaly,”-Herbert The performance of a signature verification system is generally evaluated according to the error representation of a two class pattern recognition problem, that is, with the type 1 (FRR-false rejection rate) and type 2(FAR-false acceptance rate) errors. as the ideal case (i.e., 0 percent on both errors) is questionable to exist, a choice has to be made depending on the application between one of the two error rates equal to zero or the minimization of the total error, FRR+FAR .For entry systems, the false rejection is the most important; for security systems, the false acceptance is most important.


5. Feature Extraction:
· The acquisition stage provides values of pressure exerted against a measure of time using an instrumented digital pen sensitive to pressure. A localized low pass Sum Filter of order 15 is applied to eliminate frequencies greater than 50 Hz (which could be considered as noise).
· Normalization is then conducted to standardize the values of the pressure extracted between 0 and 1. The feature extraction stage consists of two processes. A new technique of segmentation divides the time series data into specifically defined segments. Characteristics like a shape and curves of the graph, high and low points, stationary points and gradient of the graph are similar ones normalization has been done. The improve segmenting method is based on the algorithm to segment the time data graph into major curves. This is done by calculating the difference of pressure exerted between every two points. If the drop of pressure is more equal to 0.035, then a segmenting point is discovered. The first step verifying a signature is done here. If the amount of segments produced by a test signature is very different from the genuine signature being compared to, the test signature is rejected. In the second process, we use time series modeling with Auto Regressive (AR) technique to calculate the AR coefficient from all the segments. Autoregressive (AR) models have proven to be superiors to Fourier methods due to the ability of AR models to handle short segments of data while giving better frequency resolution and smoother power.
In addition AR methods need only one or more cycle of sinusoidal-type activity to be
Present in the segment to produce good spectral peaks and they also provide the ability to observe small shifts in peak frequencies, which are not easily observed with furrier derived spectra. The AR model coefficient can be easily estimated by solving recursively using Levinson- Durbin or Burg method. These coefficients are the used obtain the power spectral density (PSD) values to represent each segment. Combination of all PSD values from all the segments represents the signature.


The landing and verification stage is made up of a neural network topology known as multi-layer perception or M L P implemented as a part of recognition engine. M L P uses the incremental back propagation algorithm to train the network. Training is equivalent to finding proper weights for all the connections such that a desired output is generated for a corresponding input. Using MLP in the context of a classifier requires all output nodes to be set to 0 except for the node that is marked to correspond to the class the input is from (output equal to 1).


7. Advantages
ÿ Natural and intuitive
ÿ Commonly accepted for authentication
ÿ Less intrusive than iris, fingerprint, etc.

8. Applications of DSVS
Applications of signature identification systems have been slow in their adoption by the financial industry due to the low false rejected rates that banks and other financial institutions required. Although it has been reported that Chase Manhattan Bank was on the first bank to taste a signature identification application. Other applications of
signature identification includes:
ÿ Internal revenue service (IRS) has utilized signature identification in electronically file tax returns.
ÿ Employment Services in England to verify an individual that is claiming benefits.
ÿ Pharmaceutical companies are using it to reduce the overall cost and administration of drug regularly submissions to the FDA.


9. DSVS: Strengths and Weakness
ÿ DSVS has several strengths. Because of the large amount of data present in the signature scan template, as well as the difficulty in mimicking the behavior of signing, signature scan technology is highly resistant to impostor attempts .As a result of the low false acceptance rates (FAR), a measure of likelihood that a user claiming a false identity will be accepted, deplorers can have a high confidence level that successfully
matched users who they claim to be. Signature scan also benefit from its ability to leverage existing processes and hardware, such as signature capture tablets and systems based on the public key infrastructure PKI are popular method for data
encryption. Since most people are accustomed to providing their signatures during customer interactions, the technology is considered less invasive that some other biometrics.
ÿ However, signatures scan has several weaknesses. Signature scan is designed to verify subjects based on the threats of their unique signature. As a result, individuals who do not sign their names in a consistent manner may have difficulty enrolling and verifying in signature scan. During enrolment subjects must provide a series of signatures that are similar enough that the system can locate large percentages of the common characteristics between the enrolment signatures. During verification enough
Characteristics must remain constant to determine with confidence that the authorized
person signed. As a result, individuals with muscular illness and people who sometimes sign with only their initials might result in a higher false rejection rate (FRR), which measures the likelihood that a system will incorrectly reject an authorized user. Since many users are UN accustomed to signing on a tablet, some subject’s signatures may differ to their signatures on ink and paper, increasing the
Potential for false rejection.
ÿ The major problem faced with this technology is differentiating between the consistent parts of the signature and the behavioral parts of the signature that vary
with each signing. An individual’s signature is never entirely the same every time it is
signed and can vary substantially over an individual’s lifetime. Allowing for these variations in the system while providing the best protection against possible
forgers is an apparent hurdle faced by this technology.


10. Conclusion
ÿ Signature identification will continue to develop and improve within the biometric industry because of one major advantage:
public acceptance.
ÿ Even if the individual’s biometric measure remains solely on the card carried by the individual, a considerable level of security and privacy concern exists.
ÿ If the measure were to be stored by the third party, even if only for the purposes of backup, then a much higher level of security and privacy concern exists. A central repository for such biometric identifiers would present opportunities for social control that are the staff of antiutopian novels.















Reference
1. Zhao et al, On-line signature verification by adaptively weighted DP matching, IEICE Trans. Informat. Syst.
2. G. Lorette and R. Plamondon, Dynamic approaches to handwritten signature verification, Computer processing of handwriting, World Scientific, 1990, 21-47.
3. http\www.biomatrics.com
4. Nalwa V.S., Automatic On-line Signature Verification, Proceed. Of the IEEE, 85(2), 1997, 213-239.
5. G. K. Gupta and R. C. Joyce, A Simple Approach to Dynamic Hand-Written Signature Verification, 1995.