This document discusses signature recognition as a biometric for identity verification. It defines offline and online signatures, and describes how pen tablets digitize dynamic signatures. The document outlines the key steps of signature recognition systems, including pre-processing, feature extraction of local and global traits, modeling signatures, and detecting forgeries. It notes advantages of signatures being intuitive and independent of language, but also challenges around hardware costs and inconsistent writing abilities.
INTRO:
Nowadays, person identification (recognition) and verification is very important in security and resource access control.
Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes.
For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements.
Among the different forms of biometric recognition systems such as
fingerprint, iris, face, voice, palm etc., signature will be most widely used.
SIGNATURE RECOGNITION
Signature Recognition is the procedure of determining to whom a particular signature belongs to.
Depending on acquiring of signature images, there are two types of signature recognition systems:
Online Signature Recognition
Offline Signature Recognition
STEPS
IMAGE ACQUSITION
Collection of signatures from 50 persons on blank paper.
The collected signatures are scanned to get images in JPG format to create database.
PREPROCESSING
Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications.
The techniques for preprocessing used are
RGB to Gray Scale Conversion
Binarization
Thinning
Bounding Box
FEATURE EXTRACTION
Features are the characters to be extracted from the processed image.
It has used two feature techniques
Global Features
Grid Features
DWT
After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted.
Grid features extracted from each block are
Horizontal Projection Position
Vertical Projection Position
Algorithm for Training phase
Description: Retrieval of a signature image from a database
Input: Training sample images.
Output: Construction of Back Propagation Neural Network.
Begin
Read the training samples images
Step1: Pre-processing
Convert the image into gray scale image.
Convert the gray scale image into binary image.
Apply thinning process.
Apply bounding box.
Step 2: Features Extracted.
Step 3: Back propagation neural network training.
end // end of proposed algorithm
INTRO:
Nowadays, person identification (recognition) and verification is very important in security and resource access control.
Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes.
For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements.
Among the different forms of biometric recognition systems such as
fingerprint, iris, face, voice, palm etc., signature will be most widely used.
SIGNATURE RECOGNITION
Signature Recognition is the procedure of determining to whom a particular signature belongs to.
Depending on acquiring of signature images, there are two types of signature recognition systems:
Online Signature Recognition
Offline Signature Recognition
STEPS
IMAGE ACQUSITION
Collection of signatures from 50 persons on blank paper.
The collected signatures are scanned to get images in JPG format to create database.
PREPROCESSING
Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications.
The techniques for preprocessing used are
RGB to Gray Scale Conversion
Binarization
Thinning
Bounding Box
FEATURE EXTRACTION
Features are the characters to be extracted from the processed image.
It has used two feature techniques
Global Features
Grid Features
DWT
After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted.
Grid features extracted from each block are
Horizontal Projection Position
Vertical Projection Position
Algorithm for Training phase
Description: Retrieval of a signature image from a database
Input: Training sample images.
Output: Construction of Back Propagation Neural Network.
Begin
Read the training samples images
Step1: Pre-processing
Convert the image into gray scale image.
Convert the gray scale image into binary image.
Apply thinning process.
Apply bounding box.
Step 2: Features Extracted.
Step 3: Back propagation neural network training.
end // end of proposed algorithm
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
One of the most helpful presentation for academic and non academic purpose. This presentation can be presented for 40-45 mins. It contains both technical and non technical details of working of a fingerprint bio-metric scanner.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
Real time handwritten devanagari character recognitionYubraj Ghimire
Real Time Handwritten Devanagari Character Recognition is the android application that has the ability of to recognize the handwritten Devanagari character from an input source and translate it into digital text.
It is based on signature based algorithm and KNN algorithm.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
One of the most helpful presentation for academic and non academic purpose. This presentation can be presented for 40-45 mins. It contains both technical and non technical details of working of a fingerprint bio-metric scanner.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
Real time handwritten devanagari character recognitionYubraj Ghimire
Real Time Handwritten Devanagari Character Recognition is the android application that has the ability of to recognize the handwritten Devanagari character from an input source and translate it into digital text.
It is based on signature based algorithm and KNN algorithm.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
Data security and privacy are crucial issues to be addressed for assuring a successful deployment of biometrics-based recognition systems in real life applications. In this paper, we present a highly efficient and secured authentication scheme by online verification of signature. This authentication scheme is very simple and can be used for securing the applications and various elements which can be accessed and interfaced by touch panel’s inputs. This authentication scheme is very simple and can be used for securing the applications and various elements which can be accessed and interfaced by touch panel’s inputs.
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
Speech biometric is a revolutionary change in the field of security. Speech biometric works on the identity fication of an individual person with the help of intonation in voice. This speech biometric technology breaks down the speech in small packets of data and computer analyze and interpret. The result is extremely high voice recognition accuracy that enables natural, human-like conversations and satisfying mobile interactions. Because of this technology users in India can access any information and transact easily on their phones.
Jamming aware traffic allocation for multiple-path routing using portfolio se...Saad Bare
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
Development of biomedical technologies is an urgent necessity to improve diagnostic services.
Electronic pill technology is a more recent development.
A small miniaturized electronic pill can reach all areas such as small intestine.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Offline Handwritten Signature Verification using Neural Networkijiert bestjournal
The different biometric techniques have been discussed for ident ification. Such as face reading,fingerprint recognition and retina scanning and these are known as vision based i dentification. There are non vision based identifications such as signature verification and the voice recogni tion. Signature verification plays a vital role in the field of the financial,commercial and for the legal matters. Signature by any person considered as the approval for any work so the signature is the preferred authenticat ion. In this paper signature verification is done by means of image processing,geometric feature extraction and by using neural network technique.
Basic Definition Of Computer,Working of Computer,Components,Block Diagram,Applications,Characteristics of Computer,Types Of Computer,Convergence of Technology,2.A Functional Overview of Computer(Components) ,The Input Unit,Keyboard,Mouse,Joystick,Scanner,Optical Character Reader,
4. Signatures
Off-line or static signatures are scanned from paper
documents, where they were written in conventional way.
Off-line signature analysis can be carried out with a
scanned image of the signature using a standard camera or
scanner.
On-line or dynamic signatures are written with an
electronically instrumented device and the dynamic
information (pen tip location through time) is usually
available at high resolution, even when the pen is not in
contact with the paper.
10. Pen-Tablet System –How It Works?
The WACOM stylus looks and feels like a pen yet contains no
batteries or magnets. Instead it takes advantage of electro-magnetic
resonance technology in which radio waves are sent to the stylus and
returned for position analysis.
In operation, a grid of wires below the screen alternates between
transmit and receive modes (about every 20μs):
In transmit mode, the electro-magnetic signal stimulates oscillation in
the coil-and capacitor resonant circuit in the pen
In receive mode, the energy of the resonant circuit oscillation in the pen
is detected by the antenna grid. This is then analysed to determine
position and other information including pressure
Since the grid provides the power to the pen through resonant
coupling, no batteries are required. Thus there are no consumables
that will run down and need to be replaced or that would make the
pen top-heavy.
11. Automatic identification
Traditional means of automatic identification:
Possession-based (credit card, smart card)
Use “something that you have”
Knowledge-based (password, PIN)
Use “something that you know”
Biometrics-based (biometric identifier)
Use something that relies on “what you are”
Signature is inherently a combination of knowledge and
biometric, the knowledge component (what is written and
how it is written) can be chosen, and indeed changed, by
the user
12. Pre-processing
Smoothing: the input signal from a
digitizing pen can be very jagged. The
pen used can affect the smoothness and
the size of the signature.
Segmentation: determination of the
beginning and ending of signing.
Signature beginning: first sample
where pressure information is not null
(first pen-down)
Signature ending: last pen-up. Because
few pen-ups can be found in the
signature, we have to establish a
maximum pen-up duration (e.g. 3 s).
13. Pre-processing
Initial point
alignment: all the
signatures have to be
aligned with respect to
the initial point (e.g.
the coordinate origin)
to make information
independent from the
position on the tablet.
18. Local and Global Features
Local features
x, y coordinates
Velocity (v)
Acceleration (A)
Azimuth
Elevation
19. Local and Global Features
Global features
Signature length, height
Total signature time
Total pen-down time
Total pen-up time
Average velocity
Maximum velocity
Minimum velocity
23. Signature Recognition Advantages
Signature is a man-made biometric where forgery
has been studied extensively
Enrollment (training) is intuitive and fast
Signature verification in general has a fast
response and low storage requirements
A signature verification is “independent” of the
native language user
Very high compression rates do not affect shape
of the signature (100-150 bytes)
24. Signature Recognition Disadvantages
There is much precedence for using signature to
authenticate documents and not for security
applications
A five-dimensional pen may be needed to arrive
at the desired accuracy. This makes the hardware
costly.
Some people have palsies, while others do not
have enough fine motor coordination to write
consistently
25. References
Dynamic Signatures developed by NSTC subcomittee
on Biometrics:: www.biometrics.gov
Simina Emerich, Eugen Lupu, Corneliu Rusu, “On-
line Signature Recognition Approach Based on
Wavelets and Support Vector Machines”
Jinxu Guo, Jianbin Zheng, Bo Lu, “Research of Online
Signature Recognition Based on Energy Feature”