25-03-2011, 03:03 PM
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ASTRACT :-
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, retina scanning, facial recognition and others, but fingerprint
recognition is increasingly being acknowledged as the most practical technology for low
cost, convenient, and reliable security. Fidelica Microsystems’ new and exclusive
technology overcomes the limitations of previous systems and sets a new standard for
compact, reliable and low-cost fingerprint authentication.
THE BASIS OF FINGERPRINT AUTHENTICATION
Although fingerprints have been used as a means of identification since the middle of the
19th century, modern fingerprint authentication technology has little in common with the
ink-and-roll procedure that most people associate with fingerprinting. In order to
appreciate the distinction and understand modern fingerprint authentication technology,
one needs to understand the basis of a fingerprint.
A fingerprint is composed of ridges, the elevated lines of flesh that make up the various
patterns of the print, separated by valleys. Ridges form a variety of patterns that include
loops, whorls and arches as illustrated in Fig. 1. Minutiae are discontinuities in ridges,
and can take the form of ridge endings, bifurcations (forks), crossovers (intersections)
and many others.
Fingerprint authentication is based on a subset of features selected from the overall
fingerprint. Data from the overall fingerprint is reduced (using an algorithm application
that is usually unique to each vendor) to extract a dataset based on spatial relationships.
For example, the data might be processed to select a certain type of minutiae or a
particular series of ridges. The result is a data file that only contains the subset of data
points . the full fingerprint is not stored, and cannot be reproduced from the data file.
This is in contrast with ink-and-roll fingerprinting (or its modern optical equivalent),
which is based on the entire fingerprint.
Modern forensic fingerprinting, with files on the order of 250kB per finger, is used in large scale, one-to-many searches with huge databases, and can require hours for
verification. Fingerprint authentication, using files of less than 1000 bytes, is used for one-to-one verification and give results in a few seconds.
HOW FINGERPRINT AUTHENTICATION WORKS
In use, fingerprint authentication is very simple. First, a user enrolls in the system by
providing a fingerprint sample. The sensor captures the fingerprint image. The sensor
image is interpreted and the representative features extracted to a data file by algorithms
either on a host computer or a local processor (in applications such as cellular handsets).
This data file then serves as the users individual identification template. During the
verification process, the sequence is repeated, generating an extracted feature data file. A
pattern matching algorithm application compares the extracted feature data file to the
identification template for that user, and the match is either verified or denied. State-ofthe-art processor, algorithm and sensor systems can perform these steps in a second or
two.
MODERN FINGERPRINT AUTHENTICATION TECHNOLOGY
Fingerprint authentication can be based on optical, capacitance or ultrasound sensors.
Optical technology is the oldest and most widely used, and is a demonstrated and proven
technology, but has some important limitations. Optical sensors are bulky and costly, and
can be subject to error due to contamination and environmental effects. Capacitance
sensors, which employ silicon technology, were introduced in the late 1990’s. These
offer some important advantages compared to optical sensors and are being increasingly
applied. Ultrasound, utilizing acoustic waves, is still in its infancy and has not yet been
widely used for authentication.
I ----- SILICON-BASED SENSOR TECHNOLOGY
Silicon-based sensors have a two-dimensional array of cells, as shown in Fig. 2. The size
and spacing of the cell is designed such that each cell is a small fraction of the ridge
spacing. Cell size and spacing are generally 50 microns, yielding a resolution of up to
500 dpi, the FBI’s image standard. When a finger is placed on the sensor, activating the
transistors that underlay each individual cell captures the image. Each cell individually
records a measurement from the point on the finger directly above the cell as shown
Though different vendors use different physical properties to make the measurement, the
data is recorded as the distance, or spacing, between the sensor surface and that part of
the finger directly above it. However, distance measurement has some inherent
weaknesses, which are overcome by Fidelica Microsystems’ novel technology, as
described below.
The set of data from all cells in the sensor is integrated to form a raw, gray-scale fingerprint image as shown in Fig. 4. Fingerprint imaging using a continuum of distance
measurements results in an 8-bit gray scale image, with each bit corresponding to a
specific cell in the two-dimensional array of sensors. The extreme black and white
sections of the image correspond to low and high points on the fingerprint. Only the high
points on the fingerprint are of interest, since they correspond to the ridges on the
fingerprint that are used to uniquely identify individuals. Therefore, the 8-bit gray-scale
image must be converted into a binary, or bitonal, image using an additional procedure in the feature extraction algorithm. This process is a common source of error, since there
could be many false high points or low points due to dirt, grease, etc., each of which
could result in a false minutia extraction, and hence, introduce additional error in the
matching process.
The feature extraction algorithm is then used extract the specific features from the
fingerprint that make up the individual’s unique data file. This data file serves as the
user’s individual identification template, which is stored on the appropriate device.
During verification, the imaging and feature extraction process is repeated, and the
resulting data file compared with the users identification template by pattern matching
software to verify or deny the match.
II ------ FIDELICA MICROSYSTEMS’ TECHNOLOGY
PRESSURE SENSING SCIENCE
Fidelica Microsystems’ sensor technology is unique among commercially available
fingerprint authentication systems. Fidelica Microsystems uses a thin film-based sensor
array that measures pressure to differentiate ridges from valleys on a fingerprint. This is
in contrast to distance measurement, which is the basis of all other commercially
available sensors, whether optical or capacitance (silicon-based).
The sensor is architecturally and physically similar to the silicon-based sensors in terms of cell size and spacing, and therefore offers similar resolution. However, when a finger in placed over the sensor, only the ridges come in contact with the individual pressure sensing cells in the two-dimensional array, whereas no other part of the finger contacts the sensor. As a result, only those cells that experience the pressure from the ridges undergo a property change. To record the image, the array is scanned using proprietary electronic circuits. With an appropriate threshold setting, a distinction can be made between those cells that experience pressure and those that do not.
The Fidelica Microsystems sensor employs a resistive network at each cell location.
Each cell incorporates a structure similar to those employed in the micro-electromechanical system (MEMS) industry. Upon the application of a fingerprint, the
structures under the ridges of the fingerprint experience a deflection, and a change in
resistance results. This change in resistance is an indication of the presence of a ridge
above the cell being addressed. In principle, although the resistance value is an analog
value, the difference between the resistance in the pressed and unpressed states is large
enough that, with an appropriate threshold setting, one can easily distinguish between the
presence or absence of a ridge with high resolution and accuracy.
ASTRACT :-
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, retina scanning, facial recognition and others, but fingerprint
recognition is increasingly being acknowledged as the most practical technology for low
cost, convenient, and reliable security. Fidelica Microsystems’ new and exclusive
technology overcomes the limitations of previous systems and sets a new standard for
compact, reliable and low-cost fingerprint authentication.
THE BASIS OF FINGERPRINT AUTHENTICATION
Although fingerprints have been used as a means of identification since the middle of the
19th century, modern fingerprint authentication technology has little in common with the
ink-and-roll procedure that most people associate with fingerprinting. In order to
appreciate the distinction and understand modern fingerprint authentication technology,
one needs to understand the basis of a fingerprint.
A fingerprint is composed of ridges, the elevated lines of flesh that make up the various
patterns of the print, separated by valleys. Ridges form a variety of patterns that include
loops, whorls and arches as illustrated in Fig. 1. Minutiae are discontinuities in ridges,
and can take the form of ridge endings, bifurcations (forks), crossovers (intersections)
and many others.
Fingerprint authentication is based on a subset of features selected from the overall
fingerprint. Data from the overall fingerprint is reduced (using an algorithm application
that is usually unique to each vendor) to extract a dataset based on spatial relationships.
For example, the data might be processed to select a certain type of minutiae or a
particular series of ridges. The result is a data file that only contains the subset of data
points . the full fingerprint is not stored, and cannot be reproduced from the data file.
This is in contrast with ink-and-roll fingerprinting (or its modern optical equivalent),
which is based on the entire fingerprint.
Modern forensic fingerprinting, with files on the order of 250kB per finger, is used in large scale, one-to-many searches with huge databases, and can require hours for
verification. Fingerprint authentication, using files of less than 1000 bytes, is used for one-to-one verification and give results in a few seconds.
HOW FINGERPRINT AUTHENTICATION WORKS
In use, fingerprint authentication is very simple. First, a user enrolls in the system by
providing a fingerprint sample. The sensor captures the fingerprint image. The sensor
image is interpreted and the representative features extracted to a data file by algorithms
either on a host computer or a local processor (in applications such as cellular handsets).
This data file then serves as the users individual identification template. During the
verification process, the sequence is repeated, generating an extracted feature data file. A
pattern matching algorithm application compares the extracted feature data file to the
identification template for that user, and the match is either verified or denied. State-ofthe-art processor, algorithm and sensor systems can perform these steps in a second or
two.
MODERN FINGERPRINT AUTHENTICATION TECHNOLOGY
Fingerprint authentication can be based on optical, capacitance or ultrasound sensors.
Optical technology is the oldest and most widely used, and is a demonstrated and proven
technology, but has some important limitations. Optical sensors are bulky and costly, and
can be subject to error due to contamination and environmental effects. Capacitance
sensors, which employ silicon technology, were introduced in the late 1990’s. These
offer some important advantages compared to optical sensors and are being increasingly
applied. Ultrasound, utilizing acoustic waves, is still in its infancy and has not yet been
widely used for authentication.
I ----- SILICON-BASED SENSOR TECHNOLOGY
Silicon-based sensors have a two-dimensional array of cells, as shown in Fig. 2. The size
and spacing of the cell is designed such that each cell is a small fraction of the ridge
spacing. Cell size and spacing are generally 50 microns, yielding a resolution of up to
500 dpi, the FBI’s image standard. When a finger is placed on the sensor, activating the
transistors that underlay each individual cell captures the image. Each cell individually
records a measurement from the point on the finger directly above the cell as shown
Though different vendors use different physical properties to make the measurement, the
data is recorded as the distance, or spacing, between the sensor surface and that part of
the finger directly above it. However, distance measurement has some inherent
weaknesses, which are overcome by Fidelica Microsystems’ novel technology, as
described below.
The set of data from all cells in the sensor is integrated to form a raw, gray-scale fingerprint image as shown in Fig. 4. Fingerprint imaging using a continuum of distance
measurements results in an 8-bit gray scale image, with each bit corresponding to a
specific cell in the two-dimensional array of sensors. The extreme black and white
sections of the image correspond to low and high points on the fingerprint. Only the high
points on the fingerprint are of interest, since they correspond to the ridges on the
fingerprint that are used to uniquely identify individuals. Therefore, the 8-bit gray-scale
image must be converted into a binary, or bitonal, image using an additional procedure in the feature extraction algorithm. This process is a common source of error, since there
could be many false high points or low points due to dirt, grease, etc., each of which
could result in a false minutia extraction, and hence, introduce additional error in the
matching process.
The feature extraction algorithm is then used extract the specific features from the
fingerprint that make up the individual’s unique data file. This data file serves as the
user’s individual identification template, which is stored on the appropriate device.
During verification, the imaging and feature extraction process is repeated, and the
resulting data file compared with the users identification template by pattern matching
software to verify or deny the match.
II ------ FIDELICA MICROSYSTEMS’ TECHNOLOGY
PRESSURE SENSING SCIENCE
Fidelica Microsystems’ sensor technology is unique among commercially available
fingerprint authentication systems. Fidelica Microsystems uses a thin film-based sensor
array that measures pressure to differentiate ridges from valleys on a fingerprint. This is
in contrast to distance measurement, which is the basis of all other commercially
available sensors, whether optical or capacitance (silicon-based).
The sensor is architecturally and physically similar to the silicon-based sensors in terms of cell size and spacing, and therefore offers similar resolution. However, when a finger in placed over the sensor, only the ridges come in contact with the individual pressure sensing cells in the two-dimensional array, whereas no other part of the finger contacts the sensor. As a result, only those cells that experience the pressure from the ridges undergo a property change. To record the image, the array is scanned using proprietary electronic circuits. With an appropriate threshold setting, a distinction can be made between those cells that experience pressure and those that do not.
The Fidelica Microsystems sensor employs a resistive network at each cell location.
Each cell incorporates a structure similar to those employed in the micro-electromechanical system (MEMS) industry. Upon the application of a fingerprint, the
structures under the ridges of the fingerprint experience a deflection, and a change in
resistance results. This change in resistance is an indication of the presence of a ridge
above the cell being addressed. In principle, although the resistance value is an analog
value, the difference between the resistance in the pressed and unpressed states is large
enough that, with an appropriate threshold setting, one can easily distinguish between the
presence or absence of a ridge with high resolution and accuracy.
