Role of DSP in Biometric Applications

‘Role of DSP in
Biometric
Applications’
Dr. R. Sudarmani
Associate Professor/
Department of ECE,
School of Engineering

Signal
Signal
Differentiate Analog Signal and
Continuous Time Signal
Discrete time Signal
Digital Signal
Dr. R. Sudarmani, Dept. of ECE, Avinashilingma Institute 23.03.2019

Contd…
Analog signals are those signals which are continuous in time domain.
It may be discrete or continuous in magnitude domain (value).
Digital signals are those which are discrete in both magnitude and time domain.

Signal Processing
Signal processing is the analysis, interpretation, and manipulation
of signals like sound, images, time-varying measurement values and
sensor data etc…
•For example biological data such as electrocardiograms, control
system signals, telecommunication transmission signals such
as radio signals, and many others.
Need of Signal Processing
•Deformation of the signal by external noise
•Suitable filters are to be used. i.e the signal is processed to
obtain the pure signal.

Types of signal processing
Analog signal processing- for signals that have not been digitized, as in
classical radio, telephone, radar, and television systems.
This involves linear electronic circuits such as passive filters, active
filters, additive mixers, integrators and delay lines.
It also involves non-linear circuits such as compandors, multiplicators
(frequency mixers and voltage-controlled amplifiers), voltage-
controlled filters, voltage-controlled oscillators and phase-locked
loops.
Digital signal processing - for signals that have been digitized,
processing is done by general-purpose computers or by digital
circuits such as ASICs, Field-Programmable Gate Arrays or specialized
Digital Signal Processors (DSP chips).

The Roots of DSP
•Digital Signal Processing is distinguished from other areas in computer
science by the unique type of data it uses: signals.
•Signals originate as sensory data from the real world: seismic vibrations,
visual images, sound waves, etc.
•DSP is the mathematics, the algorithms, and the techniques used to
manipulate these signals after they have been converted into a digital form.
•This includes a wide variety of goals, such as: enhancement of visual
images, recognition and generation of speech, compression of data for
storage and transmission, etc.
•Suppose we attach an analog-to-digital converter to a computer and use it to
acquire a chunk of real world data.

History
• Up to 1950’s: signal processing done with analog systems
using electronic circuits or mechanical devices
• 1950’s: digital computers used to simulate signal processing
systems before implementing in analog hardware – cheap
way to vary parameters and test system Output
• 1965: Cooley and Tukey (re)discover efficient algorithm for
Fast Fourier Transforms (FFTs)– made feasible real-time signal
processing as well as algorithms previously thought
impossible to implement on digital computers
• 1980’s: IC technology advancements led to very fast fixed-
point and floating-point microprocessors for digital signal
processing

Contd…
•The roots of DSP are in the 1960s and 1970s when digital
computers first became available.
•Computers were expensive during this era, and DSP was limited to
only a few critical applications.
•radar & sonar, where national security was at risk
•oil exploration, where large amounts of money could be made
•space exploration, where the data are irreplaceable
•medical imaging, where lives could be saved.
•The personal computer revolution of the 1980s and 1990s caused
DSP to exploded with new applications. Rather than being motivated by
military and government needs, DSP was suddenly driven by the
commercial marketplace.
•DSP reached the public in such products as: mobile telephones,
compact disc players, and electronic voice mail etc

What is DSP
DSP is the process of extracting information from digital signals.
There are two broad classes 1. Filtering – information is in the
amplitude history of the signal 2. Spectral Analysis – information is
in frequency makeup of the signal.

Filtering

Spectral Analysis

•The analog waveform is sliced into equal segments and
the waveform amplitude is measured in the middle of
each segment
•The collection of measurements make up the digital
representation of the waveform
Contd…

Basic elements of a signal
processing system

Digital Signal Processing

DSP Components

Digital Signal Processing
By a signal we mean any variable that carries or contains some
kind of information that can be conveyed, displayed or
manipulated.
Examples of signals of particular interest are:
Speech, is encountered in telephony, radio, and everyday life

0 1 2 3 4 5 6 7 8
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
ECG Tomography
Biomedical signals(heart, brain signals)

•Sound and music, as reproduced by the compact disc player
•Video and image
•Radar signals, which are used to determine the range and bearing of
distant targets

•This technological revolution occurred from the top-down.
•In the early 1980s, DSP was taught as a graduate level
course in electrical engineering.
•A decade later, DSP had become a standard part of the
undergraduate curriculum.
•Today, DSP is a basic skill needed by scientists and
engineers.
Importance of DSP

Advantages
• Guaranteed accuracy: (accuracy is only determined by the
number of bits used) The analog circuits are prone to
temperature and external effects, but the digital filters have
no such problems.
• Perfect Reproducibility: Identical performance from unit
to unit ie. A digital recording can be copied or reproduced
several times with no loss in signal quality
• No drift in performance with temperature and age
• Uses advances in semiconductor technology to achieve:
(i) smaller size
(ii) lower cost
(iii) low power consumption
(iv) higher operating speedDr. R. Sudarmani, Dept. of ECE, Avinashilingma Institute 23.03.2019

•Greater flexibility: Reprogrammable , no need to modify the
hardware
•Superior performance- ie. linear phase response can be achieved
complex adaptive filtering becomes possible
•Digital signals can be easily stored on any magnetic media or
optical media are using semiconductor chips.
•Multiplexing:
Digital signal processing provides the way for Integrated
service digital network (ISDN) where digitized signals can
be multiplexed with other digital data and transmitted through
the same channel.

Limitations
There are also certain limitations in DSP.
•Bandwidth restrictions
•Speed limitations
•Finite word length problems

• Speed and Cost :DSP techniques are limited to signals with
relatively low bandwidths DSP designs can be expensive, especially
when large bandwidth signals are involved.
• ADC or DACs are either too expensive or do not have sufficient
resolution for wide bandwidth applications.
• The need for an ADC and DAC makes DSP not economical for
simple applications (e.g., a simple filter) and Higher power
consumption and size of a DSP implementation can make it
unsuitable for simple very low-power or small size applications
• DSP designs can be time consuming plus need the necessary
resources (software etc.)

Finite word-length problems
•If only a limited number of bits is used due to economic considerations
serious degradation in system performance may result.
•The use of finite precision arithmetic makes it necessary to quantize filter
calculations by rounding or truncation.
•Roundoff noise is that error in the filter output that results from rounding or
truncating calculations within the filter.

Applications

Steps in Digital Signal Processing
•Analog input signal is filtered to be a band-limited signal by
an input lowpass filter
•Signal is then sampled and quantized by an ADC
•Digital signal is processed by a digital circuit, often a
computer or a digital signal processor
•Processed digital signal is then converted back to an analog
signal by a DAC
•The resulting step waveform is converted to a smooth signal
by a reconstruction filter called an anti-imaging filter

Why do we need DSPs
• DSP operations require many calculations of the form:
A = B*C + D
• This simple equation involves a multiply and an add
operation
• The multiply instruction of a GPP is very slow compared with the
add instruction
•Motorola 68000 microprocessor uses
•10 clock cycles for add
•74 clock cycles for multiply

Contd…
•Digital signal processors can perform the multiply and the
add operation in just one clock cycle
•Most DSPs have a specialized instruction that causes them
to multiply, add and save the result in a single cycle
•This instruction is called a MAC (Multiply, Add, and
Accumulate)
• DSPs aim to minimize cost, power, memory use, and
development time

• A Digital Signal Processor is a special-purpose CPU (Central Processing
Unit) that provides ultra-fast instruction sequences, such as shift and add,
and multiply and add, which are commonly used in math-intensive signal
processing applications.
• A digital signal processor (DSP) is a specialized microprocessor
designed specifically for digital signal processing, generally in real
time.
• Digital Signal Processors (DSP) take real-world signals like voice,
audio,
video, temperature, pressure, or position that have been digitized and
then mathematically manipulate them. A DSP is designed for
performing mathematical functions like "add", "subtract", "multiply" and
What is digital signal processors

Digital Signal Processor Architectures
Von Neuman
•Von Neumann machines store program and data in the
same memory area with a single bus
•An instruction contains the operation command and
the address of data to be operated on (operand)
•Most of the general-purpose microprocessors such as
Motorola 68000 and Intel 80x86 use this architecture
•It is simple in hardware implementation, but the data
and program are required to share a single bus

Contd…

Multiply-accumulate hardware:
•Multiply accumulate is the most frequently used operation in
digital signal processing.
•In order to implement this efficiently, the DSP has an hardware
multiplier, an accumulator with an adequate number of bits to hold
the sum of products and at explicit multiply-accumulate
instructions.
Harvard architecture:
• In this memory architecture, there are two memory spaces.
Program memory and data memory.
•The processor core connects to these memory spaces by two
separate bus sets, allowing two simultaneous access to
memory.
This arrangement doubles the processor memoryDr. R. Sudarmani, Dept. of ECE, Avinashilingma Institute 23.03.2019
Contd…

Difference between GPP and DSP
• The main difference between a DSP and a microprocessor
is that a DSP processor has features designed to support
high-performance, repetitive, numerically intensive tasks.
• DSP processors are designed specifically to perform large
numbers of complex arithmetic calculations and as quickly
as possible.
• They are often used in applications such as image
processing, speech recognition and telecommunications.
• As compared to general microprocessors, DSP processors
are more efficient at performing basic arithmetic
operations, especially multiplication.

• DSP processor are different than General purpose processor in terms of
memory. GPP doesn’t have memory but DSP has memory on SOC.
Memory retirement is different for each application so based on that
only DSP processors designed.
•Most general-purpose microprocessors and operating systems can DSP
algorithms successfully.
•However, they are not suitable for use in portable devices such as mobile
phones. Hence, specialized digital signal processors are used.
•Digital Signal Processors have approximately the same level of integration
and the same clock frequencies as general purpose microprocessors, but they
tend to have better performance, lower latency, and no requirements for
specialized cooling or large batteries. This allows them to be a lower-cost
alternative to general-purpose microprocessors.
Contd…

Key DSP Operations
1. Convolution
2. Correlation
3. Digital Filtering
4. Discrete Transformation
5. Modulation

Convolution
Convolution is one of the most frequently used operations in DSP.
Specially in digital filtering applications where two finite and causal
sequences x[n] and h[n] of lengths N1 and N2 are convolved
where, n = 0,1,…….,(M-1) and M = N1 + N2 -1
This is a multiply and accumulate operation and DSP device
manufacturers have developed signal processors that perform this
action.

Digital Filtering
The equation for finite impulse response (FIR) filtering is
∑
−
=
−=
1
0
][][][
N
k
knxkhny
Where, x[k] and y[k] are the input and output of the filter respectively and h[k]
for k = 0,1,2,………,N-1 are the filter coefficients

[ ] [ ]∑
−
=
−⋅=
1
0
N
k
k knxbny
Filter structureFilter structure
A common filtering objective is to remove or reduce noise from a wanted signal.

Discrete Transformation
Discrete transforms allow the representation of discrete-time signals in the
frequency domain or the conversion between time and frequency domain
representations.
Many discrete transformations exists but the discrete Fourier transform
(DFT) is the most widely used one.
DFT is defined as:
N
jN
n
nk
eWwhereWnxkX
π21
0
][)(
−
−
=
== ∑
IDFT is defined as:
10,)(
1
][
1
0
−≤≤= ∑
−
=
−
NnWkX
N
nx
N
k
kn
N

Modulation
Discrete signals are rarely transmitted over long distances or stored in large
quantities in their raw form.
Signals are normally modulated to match their frequency characteristic to
those of the transmission and/or storage media to minimize signal distortion,
to utilize the available bandwidth efficiently, or to ensure that the signal have
some desirable properties.
Two application areas where the idea of modulation is extensively used are:
1. telecommunications
2. digital audio engineering
High frequency signal is the carrier
The signal we wish to transmit is the modulating signal

Three most commonly used digital modulation schemes for transmitting
Digital data over bandpass channels are:
Amplitude shift keying (ASK)
Phase shift keying (PSK)
Frequency shift keying (FSK)
When digital data is transmitted over an all digital network a scheme known
As pulse code modulation (PCM) is used.

Historical Development
First transistor: 16 December 1947, at the Bell Company by
William Shockley.

First IC: by Jack Killby 12 September 1958 at Texas
instruments Laboratories.

First Microprocessor: 15 November 1971 by Intel
Company the Intel 4004.

First DSP: 1978 : Texas Instruments TMS5100 DSP

What is Biometrics ?
• Biometrics is a technology used to identify, analyze, and measure
an individual’s physical and behavioral characteristics.
• Each human being is unique in terms of characteristics, which
make him or her different from all others. The physical attributes
such as finger prints, color of iris, color of hair, hand geometry,
and behavioral characteristics such as tone and accent of speech,
signature, or the way of typing keys of computer keyboard
etc., make a person stand separate from the rest.
• This uniqueness of a person is then used by the biometric systems
to Identify and verify a person.
• Authenticate a person to give appropriate rights of system
operations.

• Keep the system safe from unethical handling.
• Digital signal processors (DSPs), which are specially designed single- chip
digital microcomputers that process electrical signals generated by electronic
sensors (e.g., cameras, fingerprint sensors, microphones, etc.), will help to
revolutionize this world of biometrics.
• The core of the biometric authentication process is made up of image
processing and pattern matching or minutiae comparison algorithms.
• And the programmable DSP, with an architecture well-suited for
implementing complex mathematical algorithms, can efficiently address all
the processing needs of such a system.
Contd…

• The advantages of biometrics are becoming more apparent with the increasing
use of computers in our daily life.
• As cyber crime increases, the need for security against identity theft becomes
more and more apparent.
• Add to this the ever-increasing threat to personal, corporate and government
assets, the need for better forms of security is obvious Biometric authentication
will help in enhancing the security infrastructure against some of these threats.
• After all, physical characteristics are not something that can be lost, forgotten
or passed from one person to another.
• They are extremely hard to forge and a would-be criminal would think twice
before committing a crime involving biometrics.
Contd…

Elements of Biometric System
• Sensing
• Processing
• Storage
• Interface

Sensing Element
• The sensing element, or the input interface element, is the hardware
core of a biometrics system and converts human biological data into
digital form.
• Complimentary metal oxide semiconductor (CMOS) imager or a charge
coupled device (CCD)- face recognition, handprint recognition or iris/
retinal recognition systems.
• CMOS or optical sensor-fingerprint systems;.
• Microphone- voice recognition systems.
• These sensors capture the biometric information and convert it into a
digital form that can be processed by the next stage - the processing
element.

Processing Element
• The processing element is generally a microprocessor, digital
signal processor or computer that processes the data captured
from the sensors.
• The processing of the biometric image generally involves image
enhancement, normalization, template extraction, and
matching/comparison of the biometric template during
enrollment and authentication of the users.
• The DSP architecture is built to support complex mathematical
algorithms that involve a significant amount of multiplication
and addition.
• The DSP executes the multiply/add feature in a single cycle
(compared to multiple cycles for RISC processors) with the
help of the multiply/accumulate (MAC) hardware inside the
arithmetic logic unit (ALU).

• In addition, the Harvard architecture of the DSP (multiple busses)
allows instruction and operand etches in the same cycle for
increased speed of operation.
• Enhance the resolution of the captured image with the use of two-
dimensional fast fourier transforms (FFT) and finite IR filters

• The function of the storage element is to store the enrolled
template that is recalled to perform a match at the time of
authentication.
• For most identification solutions (1:N), the storage element would
be random access memory (RAM) or flash EPROM or some other
form of memory IC, and in some other cases it could be a data
server.
• In the case of verification (1:1), a removable storage element like
a contact or contactless smart card can be used.
Storage Element

• Decision of the biometric system to the interfaced asset to enable
access to the user. This can be a simple serial communication
protocol like RS232, or the higher bandwidth USB protocol.
• It could also be the TCP/IP protocol via a wired medium like
10/100 Ethernet or through a wireless medium using either the
802.11b protocol, ISM RF band, RFID, Bluetooth, or one of the
many cellular protocols.
Interface Element

Contd…
The automated use of behavioral and physiological
characteristics to determine or verify an identity.

Cont…
Biometrics Avoids…Passwords complexness like,
Too manyCan be stolen Forgotten
Protect Information for Banking Medical

DSP as a Trusted Network Device

Biometric Smart Camera

Contd…
•Biometric Smart Camera module that can be used as a digital surveillance
camera or as part of a facial recognition system based on the TMS320DM642
digital media processor.
•The DM642(Digital Media 600 MHz) processor is made up of the
C64x DSP core coupled with video ports, 10/100 EMAC controller and a
66 MHz PCI (Peripheral component Interconnect) bus in addition to standard
peripherals.
•The facial image capture can be carried out either from a snapshot (CCD
combined with data converter) or streaming video image (external camera
source via TVPXXXX video decoders) as the video ports on the DM642 are
configurable. One of the three video ports on the DM642 can be configured
to output the image to a display/monitor.
•In addition to the on-chip 10/100 Ethernet MAC controller and the 66 MHz
PCI bus that provide flexibility in terms of interface options.

Role of DSP in Biometric Applications

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Role of DSP in Biometric Applications