Soft-computing methods for robust authentication using soft-biometric data |
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Authors: | Mario Malcangi |
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Affiliation: | (1) Dipartimento di Informatica e Comunicazione, Universit? degli Studi di Milano, Via Comelico 39, 20135 Milan, Italy |
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Abstract: | Biometrics is the measurement of person’s physiological or behavioral characteristics. It enables authentication of a person’s
identity using such measurements. Biometric-based authentication is thus becoming increasingly important in computer-based
applications because the amount of sensitive data stored in such systems is growing. Particularly challenging is the implementation
of biometric-based authentication in embedded computer system applications, because the resources of such systems are scarce.
Reliability and performance are two primary requirements to be satisfied in embedded system applications. Single-mode and
hard-feature-based biometrics do not offer enough reliability and performance to satisfy such requirements. Multimode biometrics
is a primary level of improvement. Soft-biometric features can thus be considered along with hard-biometric features to further
improve performance. A combination of soft-computing methods and soft-biometric data can yield more improvements in authentication
performance by limiting requirements for memory and processing power. The multi-biometric approach also increases system reliability,
since most embedded systems can capture more than one physiological or behavioral characteristic. A multi-biometric platform
that combines voiceprint and fingerprint authentication was developed as a reference model to demonstrate the potential of
soft-computing methods and soft-biometric data. Hard-computing pattern-matching algorithms were applied to match hard-biometric
features. Artificial neural network (ANN) processing was applied to match soft-biometric features. Both hard-computing and
soft-computing matching results are inferred by a fuzzy logic engine to perform smart authentication using a decision-fusion
paradigm. The embedded implementation was based on a single-chip, floating-point, digital signal processor (DSP) to demonstrate
the practical embeddability of such an approach and the improved performance that can be attained despite limited system resources. |
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