42 – July 2008
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Fingerprint Sensor and Blackfin Processor Enhance
Biometric-Identification Equipment Design
for effective security, implemented efficiently, is manifest in today’s
world. Individuals must be identified to allow or prohibit access to secure
areas—or to enable them to use a computer, personal digital assistant
(PDA), or mobile phone. Biometric signatures, or biometrics, are
used to identify individuals by measuring certain unique physical and
behavioral characteristics. Virtually all biometric techniques are implemented
using a sensor, to acquire raw biometric data from an individual;
feature extraction, to process the acquired data to develop a feature-set
that represents the biometric trait; pattern matching, to compare
the extracted feature-set against stored templates residing in a database;
and decision-making, whereby a user’s claimed identity is authenticated
long one of the most widely accepted biometric identifiers, are unique
and permanent. Their images, formed of multiple curve segments, comprise
high areas called ridges and low areas called valleys. Minutiae,
the local discontinuities in the ridge flow pattern, are used as discriminating
features. Fingerprint sensors “read” the finger surface and convert the
analog reading into digital form through an analog-to-digital converter
(ADC). Fingerprint sensors can be broadly classified as optical, ultrasound,
or solid state—which includes capacitive, RF, thermal, and piezoelectric
Because a finger’s
outermost dry, dead skin cells have low electrical conductivity, an RF
sensor acquires fingerprint data from the skin’s moist and electrically
conductive boundary region where the live cells begin turning into keratinized
skin. This live subsurface layer is the source of the fingerprint pattern,
and it is rarely affected by damage or wear to the finger surface.
The AuthenTec® TruePrint® sensor uses a small RF signal
between a conductive layer buried inside the silicon chip and the electrically
conductive layer just below the surface of the skin. The RF field measures
the electrical potential contours of the ridges and valleys of the finger’s
underlying live epidermal layers. By acquiring data from the part of the
skin that is untainted by injury or contamination, the sensor produces
a more accurate and repeatable fingerprint sample than alternative optical
or capacitive technologies that read only the surface of the skin.
materials generate a voltage based on temperature differentials. When
a finger is in contact with a warmed sensor’s surface, the fingerprint
ridges—which are closer to the sensor surface—retain a higher temperature
than the valleys, which are farther from the sensor surface. The Atmel® AT77C104B
captures fingerprints using this type of thermal imaging. A linear sensor,
it combines detection and data conversion circuitry in a single CMOS IC.
Fingerprint images are captured by sweeping the finger over a sensing
area. An image is produced when contact first occurs, but because it soon
disappears as thermal equilibrium is reached, a sweeping method is necessary
to acquire a stable fingerprint image.
The sensor, shown
in Figure 1, captures the image of a fingerprint as the finger is swept
vertically over the sensor window, as shown in Figure 2. The finger sweep
technology ensures that the sensor surface stays clean. Unlike touch-based
sensors, latent fingerprints do not remain once the finger has been removed.
The sensor requires no external heat, light, or radio source. On-chip
temperature stabilization identifies a temperature difference between
the finger and the sensor, and increases the difference for higher image
contrast. The discussion here will focus on a fingerprint recognition
system based on this type of thermal sensor.
1. AT77C104B FingerChip IC.
2. Typical fingerprint—and finger being swept over a sensor.
The main parameters
that characterize fingerprint sensors include resolution, area,
dynamic range, and number of pixels. Resolution is
measured in dots (or pixels) per inch (dpi). Higher resolution allows
better definition between ridges and valleys, and finer isolation of minutiae
points—which play a primary role in fingerprint matching, since most algorithms
rely on the coincidence of minutiae to determine if two fingerprint impressions
are of the same finger. Larger sensing areas generally provide
a more distinctive fingerprint, but sweeping the finger over a smaller
sensor, and acquiring and processing the data rapidly, allows a small,
low-cost sensor to achieve comparable definition to larger, more expensive
sensors. Dynamic range, or depth, denotes the number of bits used
to encode the intensity of each pixel. The number of pixels in
the fingerprint image in a particular frame can be derived from the resolution
The AT77C104B sensor
has 500-dpi resolution over a 0.4 mm × 11.6 mm area,
providing a total of 8 pixels × 232 pixels,
or 1856 pixels per frame. Each pixel is encoded with four bits, identifying
16 grayscale levels. Figure 3 shows a block diagram of the sensor, which
includes the array, analog-to-digital converter, on-chip oscillator, control
and status registers, navigation and click units, and separate interfaces
for slow and fast modes of operation. Slow mode, which can
run at up to 200 kHz, is used to program, control, and configure the sensor.
Fast mode, which can run at up to 16 MHz, is used for data acquisition.
An on-chip heater increases the temperature difference between the finger
and the sensor. To limit current consumption, a watchdog timer stops heating
the module after a specified length of time.
3. Block diagram of the fingerprint sensor.
Modes of Operation
implements six modes of operation:
mode: A very low-power-consumption mode, in which the internal clocks
are disabled and the registers are initialized.
mode: A low-power-consumption mode, waiting for action from the host.
The slow serial port interface (SSPI) and control blocks are activated;
the oscillator remains active.
mode: Waiting for a finger on the sensor. The SSPI and control blocks
remain active; the local oscillator, click array, and click block are
mode: Calculating x- and y movements as the finger crosses
the sensor. The SSPI and control blocks are still activated; the local
oscillator, navigation array, and navigation block are also activated.
mode: Slices are sent to the host for fingerprint reconstruction and
identification. The SSPI and control blocks are still activated; the
fast serial port interface block (FSPI) and the acquisition array are
activated. The local oscillator is activated when a watchdog timer is
- Test mode:
This mode is reserved for factory testing.
Fingerprint Sensor to the Blackfin®
Processor’s Serial Peripheral Interface
low-cost, high-performance processor is chosen for this application
because it combines the functions of a fast signal processor and a powerful
microcontroller. Its 4-wire, full-duplex synchronous serial peripheral
interface (SPI) has two data pins (MOSI and MISO), a device-select pin
(/SPISS), and a gated clock pin (SCK). See Figure 4. The SPI supports
master modes, slave modes, and multimaster environments.
The SPI-compatible peripheral implementation also supports programmable
baud rate and clock phase/polarities.
4. Block diagram of Blackfin processor’s SPI port.
The interface is
essentially a shift register that serially transmits and receives data
bits—one bit at a time, at the SCK rate—to and from other SPI devices.
The shift register enables the simultaneous transmission and reception
of serial data. The SCK synchronizes the shifting and sampling of the
data on the two serial data pins.
The SPI port can
be configured as master (generates SCK and /SPISS signals) or slave
(receives SCK and slave select signals externally). When the SPI port
is configured as master, it drives data on the MOSI pin and receives data
on the MISO pin. It drives the slave select signals for SPI slave devices
and provides the serial bit clock (SCK). The Blackfin processor’s SPI
supports four functional modes by using combinations provided by the clock
polarity (CPOL) and clock phase (CPHA) bits. For detailed information
on the Blackfin SPI port, refer to the
Blackfin Processor Hardware Reference Manual.
hardware interface between the ADSP-BF533 processor’s SPI port and the
AT77C104B, shown in Figure 5, does not require any external glue logic.
The slave select signals of the sensor, /SSS and /FSS, are driven
through programmable flag pins PF1 and PF2. One flag should be configured
as an output and driven high before the other flag is configured as an
output (these flags should never be simultaneously configured as outputs,
as the Blackfin processor, driving them low by default, would switch the
sensor chip to scan test mode). Sensor interrupts, generated through the
/IRQ pin, are read by input PF4. The reset, RST, is driven by PF3. Reset
is an active-high signal, so a pull-down resistor is used on this line.
5. Interface between ADSP-BF533 processor and AT77104B FingerChip sensor.
code performs tasks such as controlling the sensor, acquiring fingerprint
data, and rearranging the data to display the received fingerprint image
VisualDSP++® development tool’s Image Viewer
When the sensor
detects a click (i.e., a signal indicating the presence of a finger),
it generates an interrupt. The Blackfin processor receives this interrupt,
and generates an interrupt on a falling edge. The STATUS register indicates
the event that caused the interrupt. This process is used for navigation,
read error, and other interrupts. A simplified flow chart of the complete
application is shown in Figure 6.
6. Application flow chart.
heating is enabled in acquisition mode. The watchdog timer is also enabled,
ensuring that heating remains controlled. Thus, when heating is requested,
the sensor is heated for “n” seconds.
DMA parameters are
then set up for data acquisition. Variable-size DMA flex descriptors are
loaded into DMA parameter registers. The sequence of registers is essentially
fixed, but the length of the descriptor is completely programmable. A
2D array is used to configure the DMA parameters. The 1D arrays are the
individual descriptors. The first descriptor, a dummy, is used to receive
the first five bytes because 40 dummy clock cycles must be sent by the
sensor before the first data arrives in order to initialize the chip pipeline.
Thus, the first synchronization sequences appear after 40 clock cycles.
Data then arrives at every clock cycle for all following array readings.
The sensor sends
data in the form of frames. The start of each frame is marked by
the dummy column, which contains a synchronization word. The pixel array
is read top to bottom, column by column, from the top left to the bottom
must be rearranged to display the acquired fingerprint image. The rearranged
data is stored and can be viewed using the VisualDSP++ Image Viewer utility.
The acquired image and settings are shown in Figure 7. The following functions
The sensor sends data in a nibble-swapped format. A routine swaps the
odd-even pixels for the entire frame.
- 4-bit to 8-bit
conversion: Each sensor pixel is 4 bits wide, but the Image Viewer
displays images with 8-pixel minimum width. Four bits of zero-padding
converts each pixel to 8 bits.
- Level adjustment:
Each pixel in the received data has an intensity of 0 to 15, but the
display range is 0 to 255. Level translation of each pixel produces
a good display.
- Array transpose:
The data from the sensor is sent column-wise, but the 2-dimensional
DMA receives data row-wise, so it must be transposed in order to display
the frames continuously. A 3-dimensional array is used to get a continuous
display of frames.
7. VisualDSP++ screen shot for image capture.
fingertip is swept across the sensor window at a reasonable rate, the
overlap between successive frames enables an image of the entire fingerprint
to be reconstructed using software supplied by Atmel. The reconstructed
image is typically
25 mm × 14 mm, or 500
pixels × 280 pixels,
with 8-bit resolution due to resolution enhancement. Each image thus requires
140 kB of storage. Larger or smaller images can be derived from this using
standard image-processing techniques. Once the frames have been joined
to obtain a complete fingerprint image, recognition algorithms can match
the sample with a template.
Trust but Verify
processing has three primary functions: enroll, search,
and verify. Enrollment acquires a fingerprint image from
the sensor and saves it in SRAM. The image is processed, enhanced, and
compressed to create a fingerprint template. Various filters clean up
the image and convert it to a mathematical representation, making it impossible
to steal a template and directly recreate a fingerprint image.
a raw candidate image to a list of previously enrolled templates. Through
a series of screening processes, the algorithm narrows the list of templates
to a manageable size. Those templates that survive screening are compared
to the candidate and verification scores are provided. A score exceeding
a preset threshold indicates a positive identification.
validates a user’s identity by comparing a raw candidate image to a previously
enrolled template via real-time, closed-loop pattern-matching algorithms.
A score is returned indicating the similarity of the candidate and template
to generate a yes/no match decision.
processor and AT77C104B FingerChip sensor combine to provide simple, yet
robust, fingerprint identification, enhancing security by allowing or
prohibiting access to sensitive areas in buildings or sensitive data in
Blackfin Processor Hardware Programming Reference.
Analog Devices. 2007.
ADSP-BF532/ADSP-BF533 Blackfin Embedded Processor Data Sheet
Data Sheet, “FingerChip Thermal Fingerprint Sweep
Sensor.” Atmel Corporation.
- “AuthenTec Speeds
Fingerprint Matching with Blackfin.” Blackfin Customer Case Studies.
Atmel Fingerprint Sensor AT77C104B with Blackfin Processors.
Engineer to Engineer Note EE-325. Aug 2007.
- Kreitzer, Kelvin
and Alan Kasten. “New
Fingerprint Subsystem Brings Biometrics to the Mass Market.”
Embedded Computing Design. 2007.
- Maltoni, David,
Dario Maio, Anil K. Jain, and Salil Prabhakar. Handbook
of Fingerprint Recognition. Jun 2003.
is a DSP applications engineer with the India Product Development
Center (IPDC) Applications Group in Bangalore, India. She joined Analog
Devices in June 2006 after graduating from National Institute of Technology,
Warangal, with a B.Tech in electronics and communications engineering.
She works primarily on application development involving Blackfin
joined ADI in 2002 as a processor applications engineer, located
in Bangalore, India. Previously he worked as a senior system design
and VLSI engineer at Tata Infotech, Wipro Technologies, and Force
Computers. He received a BE in electrical and electronics engineering
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