SOFTWARE AND HARDWARE REQUIREMENTS
SOFTWARE REQUIREMENTS:
v Windows 98 or later.
v Windows NT 4.0 or later.
v Microsoft VC++ 6.0
v TWAIN Support
Monday, December 24, 2007
SOFTWARE DETAILS
SOFTWARE DETAILS
LANGUAGE FEATURES
Online Help
Developer Studio includes a completely new online help system based on
HTML. Developer Studio allows to accesses help in four ways.
* By book
* By topic
* Byword
* By help
LANGUAGE FEATURES
Online Help
Developer Studio includes a completely new online help system based on
HTML. Developer Studio allows to accesses help in four ways.
* By book
* By topic
* Byword
* By help
PC TO MICROCONTROLLER COMMUNICATION PROGRAM
PC TO MICROCONTROLLER COMMUNICATION PROGRAM
$MOD51
RSINBUF EQU 030H
RSOUT EQU 031H
SERMOD1 EQU 040H
PINBUF EQU 031H
PINBIT EQU 020H.1
$MOD51
RSINBUF EQU 030H
RSOUT EQU 031H
SERMOD1 EQU 040H
PINBUF EQU 031H
PINBIT EQU 020H.1
USES:
USES:
In summary, the printer port affords a very simple technique for interfacing with external circuitry. Twelve output bits are available, eight on the Data Port and four on the lower nibble of the Control Port. Inversions are necessary on three of the bits on the Control Port. Five inputs are available on the Status Port. One software inversion is necessary when reading these bits.
In summary, the printer port affords a very simple technique for interfacing with external circuitry. Twelve output bits are available, eight on the Data Port and four on the lower nibble of the Control Port. Inversions are necessary on three of the bits on the Control Port. Five inputs are available on the Status Port. One software inversion is necessary when reading these bits.
RS-232
RS-232
Serial Port
RS-232 was created for one purpose, to interface between Data Terminal Equipment (DTE) and Data Communications Equipment (DCE) employing serial binary data interchange. So as stated the DTE is the terminal or computer and the DCE is the modem or other communications device.
Serial Port
RS-232 was created for one purpose, to interface between Data Terminal Equipment (DTE) and Data Communications Equipment (DCE) employing serial binary data interchange. So as stated the DTE is the terminal or computer and the DCE is the modem or other communications device.
HARDWARE DETAILS
HARDWARE DETAILS
RS-232 Level Converters:
Almost all digital devices which we use require either CMOS (chemical metal oxide semiconductor) logic levels. Therefore the first step to connecting a device to the RS-232 port is to transform the RS-232 levels back into 0 and 5 Volts. As we have already covered, this is done by RS-232 Level Converters. Two common RS-232 Level Converters are the 1488 RS-232 Driver and the 1489 RS-232 Receiver. Each package contains 4 inverters of the one type, either Drivers or Receivers. The driver requires two supply rails, +7.5 to +15v and -7.5 to -15v. As you could imagine this may pose a problem in many instances where only a single supply of +5V is present. However the advantages of these IC's are they are cheap. Another device is the MAX-232. It includes a Charge Pump, which generates +10V and -10V from a single 5v supply. This I.C. also includes two receivers and two transmitters in the same package. This is handy in many cases when you only want to use the Transmit and Receive data Lines. You don't need to use two chips, one for the receive line and one for the transmit. However all this convenience comes at a price, but compared with the price of designing a new power supply it is very cheap. There are also many variations of these devices. The large values of capacitors are not only bulky, but also expensive. Therefore other devices are available which use smaller capacitors and even some with inbuilt capacitors. (Note: Some MAX-2321 can use 1 microfarad Capacitors). However the MAX-232 is the most common,
RS-232 Level Converters:
Almost all digital devices which we use require either CMOS (chemical metal oxide semiconductor) logic levels. Therefore the first step to connecting a device to the RS-232 port is to transform the RS-232 levels back into 0 and 5 Volts. As we have already covered, this is done by RS-232 Level Converters. Two common RS-232 Level Converters are the 1488 RS-232 Driver and the 1489 RS-232 Receiver. Each package contains 4 inverters of the one type, either Drivers or Receivers. The driver requires two supply rails, +7.5 to +15v and -7.5 to -15v. As you could imagine this may pose a problem in many instances where only a single supply of +5V is present. However the advantages of these IC's are they are cheap. Another device is the MAX-232. It includes a Charge Pump, which generates +10V and -10V from a single 5v supply. This I.C. also includes two receivers and two transmitters in the same package. This is handy in many cases when you only want to use the Transmit and Receive data Lines. You don't need to use two chips, one for the receive line and one for the transmit. However all this convenience comes at a price, but compared with the price of designing a new power supply it is very cheap. There are also many variations of these devices. The large values of capacitors are not only bulky, but also expensive. Therefore other devices are available which use smaller capacitors and even some with inbuilt capacitors. (Note: Some MAX-2321 can use 1 microfarad Capacitors). However the MAX-232 is the most common,
TRAINING AND TESTING OF NEURAL NETWORKS:
TRAINING AND TESTING OF NEURAL NETWORKS:
TRAINING
The process of training the neural system is like newly studying a subject. This a time consuming process. The system while training is given both the input and its corresponding output when used under the supervised learning mode.
TESTING
The neural system will also take only a short time to answer the questions posed to it. It is to note that only the questions are posed to us and not is answer and so we give only the input to the neural system while testing the neural system.
TRAINING
The process of training the neural system is like newly studying a subject. This a time consuming process. The system while training is given both the input and its corresponding output when used under the supervised learning mode.
TESTING
The neural system will also take only a short time to answer the questions posed to it. It is to note that only the questions are posed to us and not is answer and so we give only the input to the neural system while testing the neural system.
NEURAL NETWORK ARCHITECTURE:
NEURAL NETWORK ARCHITECTURE:
The architecture may generally be classified as fallows:
v Feed forward network
v Feed back network
v Recurrent network
v Lattice network
The architecture may generally be classified as fallows:
v Feed forward network
v Feed back network
v Recurrent network
v Lattice network
NEURAL NETWORK
NEURAL NETWORK – a lookup
Image processing is used for comparison of the already stored patterns with the include test pattern image. The input image pattern was compared each time when prompted.
ARTIFICIAL NEURAL NETWORK:
Artificial neural network (ANN) is richly connected networks of simple computational elements that are used to carry out complex cognitive and computational task. It is also called “parallel distributed processing (PDP) models”.
Image processing is used for comparison of the already stored patterns with the include test pattern image. The input image pattern was compared each time when prompted.
ARTIFICIAL NEURAL NETWORK:
Artificial neural network (ANN) is richly connected networks of simple computational elements that are used to carry out complex cognitive and computational task. It is also called “parallel distributed processing (PDP) models”.
Thinness:
Thinness:
Thinness = 4 *3.14 [area / perimeter ^2]
Histogram:
Histogram is used as the model of the probability distribution of gray levels.
P (g) = N (g) / M
Where,
P (g) is the first order Histogram probability
N (g) is the number of pixels at the specified gray level
M is the total number of pixels
Mean:
Means is the average value, so it tells about the brightness of the image.
Variance:
Variance of the ridge valley structures in the fore ground is higher than the variance of the noise in the background.
Standard Deviation:
The standard deviation of the ridge valley structures in the fore ground is higher than the standard deviation of the noise in the background.
Ridge ending:
The ridge ending is calculated by counting the number of active pixels found on minutiae detected fingerprint image.
Ridge bifurcation:
The ridge bifurcation is calculated by counting the number of active pixels forming the bifurcation object from the minutiae detected finger print image.
Thinness = 4 *3.14 [area / perimeter ^2]
Histogram:
Histogram is used as the model of the probability distribution of gray levels.
P (g) = N (g) / M
Where,
P (g) is the first order Histogram probability
N (g) is the number of pixels at the specified gray level
M is the total number of pixels
Mean:
Means is the average value, so it tells about the brightness of the image.
Variance:
Variance of the ridge valley structures in the fore ground is higher than the variance of the noise in the background.
Standard Deviation:
The standard deviation of the ridge valley structures in the fore ground is higher than the standard deviation of the noise in the background.
Ridge ending:
The ridge ending is calculated by counting the number of active pixels found on minutiae detected fingerprint image.
Ridge bifurcation:
The ridge bifurcation is calculated by counting the number of active pixels forming the bifurcation object from the minutiae detected finger print image.
CALCULATION OF FEATURES
CALCULATION OF FEATURES
Aspect ratio:
It is also called a elongation or eccentricity, defined by the ratio of bounding box of an object.
Aspect ratio = Cmax - Cmin + 1 / Rmax – Rmin +1
Area:
Product of the image height and the width
Area = height of the image * width of the image
Perimeter:
Twice the product of the image height and width
Perimeter = 2 * (height of the image + width of the image)
Aspect ratio:
It is also called a elongation or eccentricity, defined by the ratio of bounding box of an object.
Aspect ratio = Cmax - Cmin + 1 / Rmax – Rmin +1
Area:
Product of the image height and the width
Area = height of the image * width of the image
Perimeter:
Twice the product of the image height and width
Perimeter = 2 * (height of the image + width of the image)
TYPES OF COLOR MODELS:
TYPES OF COLOR MODELS:
There are five basic types of color models as given below:
v RGB color model
v CMY color model
v YIZ color model
v HIS color model
v HSV color model
There are five basic types of color models as given below:
v RGB color model
v CMY color model
v YIZ color model
v HIS color model
v HSV color model
PIXEL DIMENTIONS:
PIXEL DIMENTIONS:
The number of pixels along the height and width of a bitmap image defines the size of the image.
IMAGE RESOLUTION:
The number of pixels displayed per unit of printed length in an image, usually measured in pixels per inch.
MONITOR RESOLUTION:
The number of pixels or dots displayed per unit of length on the monitor usually measured in dots per inch (dpi) monitor resolution depends on the size of the monitor plus its pixel settings.
The number of pixels along the height and width of a bitmap image defines the size of the image.
IMAGE RESOLUTION:
The number of pixels displayed per unit of printed length in an image, usually measured in pixels per inch.
MONITOR RESOLUTION:
The number of pixels or dots displayed per unit of length on the monitor usually measured in dots per inch (dpi) monitor resolution depends on the size of the monitor plus its pixel settings.
IMAGE PRCESSING
IMAGE PRCESSING - ahead
BITMAP IMAGES
All images editing software’s generate bitmap images; these bitmap images are also referred to as Raster images. Bitmap images use a grid of Small Square known as pixels to represent images.
VECTOR GRAPHICS
Vector graphics are made of lines and curves defined by mathematical objects. A vector graphics is resolution independent.
BITMAP IMAGES
All images editing software’s generate bitmap images; these bitmap images are also referred to as Raster images. Bitmap images use a grid of Small Square known as pixels to represent images.
VECTOR GRAPHICS
Vector graphics are made of lines and curves defined by mathematical objects. A vector graphics is resolution independent.
Whorl:
Whorl:
There are two deltas; they are sub-divided by their core trends
v Clock wise
v Anticlockwise
v Other
d) Composite:
The competitively unknown type of pattern being one of the complicated combinations of pattern a minority pattern, which does not confirm to the arch, loop or whorl type yet posses the characteristics come to all three types.
v Unusual patterns
v Scarred prints
v Amputated fingers
There are two deltas; they are sub-divided by their core trends
v Clock wise
v Anticlockwise
v Other
d) Composite:
The competitively unknown type of pattern being one of the complicated combinations of pattern a minority pattern, which does not confirm to the arch, loop or whorl type yet posses the characteristics come to all three types.
v Unusual patterns
v Scarred prints
v Amputated fingers
LOOP:
LOOP:
The next major classification in the pattern is loop. They are further classified as fallows:
v Radial loop
o Plain
o Converging
o Nut ant
v Ulnae loop
o Plain
o Converging
o Nut ant
v Central packet loop
o Inner tracing
o Outer tracing
The next major classification in the pattern is loop. They are further classified as fallows:
v Radial loop
o Plain
o Converging
o Nut ant
v Ulnae loop
o Plain
o Converging
o Nut ant
v Central packet loop
o Inner tracing
o Outer tracing
ARCH:
a) ARCH:
From the name implies the fingerprint pattern appearance like ARCH. In this, the ridges run from side to side making no backward turn. This fingerprint pattern is further classified as fallows:
v Plain arch
v Approximately radial loop or radial arch
v Approximately ulnae loop
v Tented arch
From the name implies the fingerprint pattern appearance like ARCH. In this, the ridges run from side to side making no backward turn. This fingerprint pattern is further classified as fallows:
v Plain arch
v Approximately radial loop or radial arch
v Approximately ulnae loop
v Tented arch
Recurrence:
Recurrence:
After injury or any cut in the finger the fingerprint is well reformed and also it will be identical to that of the fingerprint which already existed that is, there will be no change between the older one and the newly formed finger print one.
Fingerprint classification:
There exist two types of process namely identification and verification. In identification there are two techniques namely computerized digital filtering and pattern recognition.
A fingerprint pattern can conceptually be partitioned into three areas namely core area, marginal area and base area. The ridges from these areas meet at the triangular formation called delta area.
Depending on the ridge flow in the core and the number or delta points, fingerprints are grouped into four major classes or types or patterns vise ARCH, LOOP, WHORL and COMPOSITE.
Ridge:
Raised portions of the fingerprints are called as ridges.
Delta:
Delta point is a bi-radial point with three ridges radiations from it.
Core:
The core point is the top most point on the inner ridge alternatively it can be defined as the inner most point of the fingerprint.
After injury or any cut in the finger the fingerprint is well reformed and also it will be identical to that of the fingerprint which already existed that is, there will be no change between the older one and the newly formed finger print one.
Fingerprint classification:
There exist two types of process namely identification and verification. In identification there are two techniques namely computerized digital filtering and pattern recognition.
A fingerprint pattern can conceptually be partitioned into three areas namely core area, marginal area and base area. The ridges from these areas meet at the triangular formation called delta area.
Depending on the ridge flow in the core and the number or delta points, fingerprints are grouped into four major classes or types or patterns vise ARCH, LOOP, WHORL and COMPOSITE.
Ridge:
Raised portions of the fingerprints are called as ridges.
Delta:
Delta point is a bi-radial point with three ridges radiations from it.
Core:
The core point is the top most point on the inner ridge alternatively it can be defined as the inner most point of the fingerprint.
FINGERPRINT
FINGERPRINT - a close look
The flow pattern of ridges in a fingerprint is unique to the person in that no two people with same fingerprints have yet been found.
Uses of fingerprints for person identification are due to the following three qualities that a fingerprint,
v Uniqueness
v Permanence
v Recurrence
Uniqueness:
The quality is very clear from the heading itself, that is no fingerprint is the same
Unless they are taken from same finger of the same person.
Permanence:
This is obvious; the fingerprint doesn’t change through out the life period that is from birth to death.
The flow pattern of ridges in a fingerprint is unique to the person in that no two people with same fingerprints have yet been found.
Uses of fingerprints for person identification are due to the following three qualities that a fingerprint,
v Uniqueness
v Permanence
v Recurrence
Uniqueness:
The quality is very clear from the heading itself, that is no fingerprint is the same
Unless they are taken from same finger of the same person.
Permanence:
This is obvious; the fingerprint doesn’t change through out the life period that is from birth to death.
BIOMETRICS
BIOMETRICS – an overview
Biometrics, the science of applying unique physical or behavioral characteristics to verify an individual’s identity, is the basis for a variety of rapidly expanding applications for both data security and access control. Numerous biometrics approaches currently exist, including voice recognition, iris scanning. Facial recognition and others, but fingerprint recognition is increasingly being acknowledged as the most practical technology for low cost, convent and reliable security. The project provides new standard for compact, reliable and low-cost fingerprint authentication.
The manual method of fingerprint indexing results in a highly skewed distribution of fingerprints into bins (types): most fingerprints fell into a few bins and this resulted in search inefficiencies. Fingerprint training procedures were time-intensive and slow. Further, demands imposed by painstaking attention needed to visually match the fingerprints of varied qualities, tedium of monotonic nature of the work, and increasing workloads due to a higher demand on fingerprint identification services, all prompted the law enforcement agencies to initiate research into acquiring fingerprints through electronic medium and automatic fingerprint identification based on the digital representation of the fingerprints. These efforts have led to development of automatic, semi-automatic fingerprint identification systems over the past few decades. We attempt to present current state-of-the-art in fingerprints sensing and identification technology.
Biometrics, the science of applying unique physical or behavioral characteristics to verify an individual’s identity, is the basis for a variety of rapidly expanding applications for both data security and access control. Numerous biometrics approaches currently exist, including voice recognition, iris scanning. Facial recognition and others, but fingerprint recognition is increasingly being acknowledged as the most practical technology for low cost, convent and reliable security. The project provides new standard for compact, reliable and low-cost fingerprint authentication.
The manual method of fingerprint indexing results in a highly skewed distribution of fingerprints into bins (types): most fingerprints fell into a few bins and this resulted in search inefficiencies. Fingerprint training procedures were time-intensive and slow. Further, demands imposed by painstaking attention needed to visually match the fingerprints of varied qualities, tedium of monotonic nature of the work, and increasing workloads due to a higher demand on fingerprint identification services, all prompted the law enforcement agencies to initiate research into acquiring fingerprints through electronic medium and automatic fingerprint identification based on the digital representation of the fingerprints. These efforts have led to development of automatic, semi-automatic fingerprint identification systems over the past few decades. We attempt to present current state-of-the-art in fingerprints sensing and identification technology.
main feature
The main feature employed in this project is area and perimeter, mean, variance, standard deviation, histogram and ridge ending, ridge bifurcation.
In this project we adopt the neural based approach for pattern recognition which over comes the disadvantages of the proposed method. The neural network applied in this project is Back Propagation Network. The back propagation network work efficiently for the feature based fingerprint classification and verification.
In this project we adopt the neural based approach for pattern recognition which over comes the disadvantages of the proposed method. The neural network applied in this project is Back Propagation Network. The back propagation network work efficiently for the feature based fingerprint classification and verification.
attribute
The possible fingerprint recognition system should contain the following attributes:
v It must be automatic
v Use dedicated hardware
v Be fast
v And very reliable
v It must be automatic
v Use dedicated hardware
v Be fast
v And very reliable
INTRODUCTION
INTRODUCTION
GENERAL OVERVIEW
Biometrics, the science of applying unique physical or behavioral characteristics to verify an individual’s identity, is the basis for the variety of rapidly expanding applications for both data security and access control. Numerous Biometrics approaches currently exist, including voice recognition, Iris scanning, facial recognition and others but fingerprint recognition is increasingly being acknowledged as the most practical technology for low cost, convenient and reliable security.
Fingerprint comparison is a fundamental method for identification of people. Fingerprint identification is based on immutability and the individuality of fingerprints.
Because of the large collection of fingerprint images and recent advancement in computer technology there has been increasing interest in automatic classification of fingerprints.
Identification and verification is the process where by an unknown print is matched against all the prints. Verification is the matching of print from a known class to the prints of same class.
GENERAL OVERVIEW
Biometrics, the science of applying unique physical or behavioral characteristics to verify an individual’s identity, is the basis for the variety of rapidly expanding applications for both data security and access control. Numerous Biometrics approaches currently exist, including voice recognition, Iris scanning, facial recognition and others but fingerprint recognition is increasingly being acknowledged as the most practical technology for low cost, convenient and reliable security.
Fingerprint comparison is a fundamental method for identification of people. Fingerprint identification is based on immutability and the individuality of fingerprints.
Because of the large collection of fingerprint images and recent advancement in computer technology there has been increasing interest in automatic classification of fingerprints.
Identification and verification is the process where by an unknown print is matched against all the prints. Verification is the matching of print from a known class to the prints of same class.
Subscribe to:
Posts (Atom)