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.

Soft-computing methods for robust authentication using soft-biometric data / M. Malcangi. - In: NEURAL COMPUTING & APPLICATIONS. - ISSN 0941-0643. - 20:6(2011), pp. 865-877.

Soft-computing methods for robust authentication using soft-biometric data

M. Malcangi
Primo
2011

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.
Artificial neural networks; Digital signal processor; Embedded personal authentication systems; Fuzzy logic engine; Multi-biometrics; Soft-biometric data
Settore INF/01 - Informatica
2011
Article (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/160897
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
social impact