15-06-2013, 04:52 PM
Quality Measures in Biometric Systems
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INTRODUCTION
Biometric recognition is a mature technology used
in many government and civilian applications such
as e-passports, ID cards, and border control. Examples
include the US-Visit (United States Visitor and Immigrant
Status Indicator Technology) fingerprint system,
the Privium iris system at Schiphol airport, and the
SmartGate face system at Sydney Airport.
However, during the past few years, biometric quality
measurement has become an important concern
after biometric systems’ poor performance on pathological
samples. Studies and benchmarks have shown that
biometric signals’ quality heavily affects biometric system
performance. This operationally important step has
nevertheless received little research compared to the primary
feature-extraction and pattern-recognition tasks.
Many factors can affect biometric signals’ quality,
and quality measures can play many roles in biometric
systems. Here, we summarize the state of the art in quality
measures for biometric systems, giving an overall
framework for the challenges involved.
How Signal Quality
Affects System Performance
One of the main challenges facing biometric technologies
is performance degradation in less controlled situations.
1 The proliferation of portable handheld devices
with at-a-distance and on-the-move biometric acquisition
capabilities are just two examples of nonideal scenarios
that aren’t sufficiently mature. These will require
robust recognition algorithms that can handle a range of
changing characteristics.2 Another important example
is forensics, in which intrinsic operational factors further
degrade recognition performance and generally
aren’t replicated in controlled studies.
What Is Biometric Sample Quality?
Broadly, a biometric sample is of good quality if it’s suitable
for personal recognition. Recent standardization
efforts (ISO/IEC 29794-1) have established three components
of biometric-sample quality (see Figure 3):
character indicates the source’s inherent discriminative
capability;
fidelity is the degree of similarity between the sample
and its source, attributable to each step through which
the sample is processed; and
utility is a sample’s impact on the biometric system’s
overall performance.
The character and fidelity contribute to or detract from
the sample’s utility.1
User-Sensor Interaction Factors
In principle, these factors, which include environmental
and operational factors, are easier to control than
user-related factors, provided that we can supervise
the interaction between the user and the sensor—
for example, in controllable premises. Unfortunately,
requirements of less controlled scenarios, such as
mobility or remoteness, make a biometric algorithm
to account for environmental or operational variability
necessary.
Acquisition Sensor Factors
In most cases, the sensor is the only physical point
of interaction between the user and the biometric
system. Its fidelity in reproducing the original biometric
pattern is crucial for the recognition system’s
accuracy. The diffusion of low-cost sensors and portable
devices (such as mobile cameras, webcams, telephones
and PDAs with touchscreen displays, and so
on) is rapidly growing in the context of convergence
and ubiquitous access to information and services.
This represents a new scenario for automatic biometric
recognition systems.