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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Ciota Z (2001) Improvement of Speech Processing Using Fuzzy Logic Approach. In: Proceedings of IFSAWorld congress and 20th NAFIPS international conference
Bosteels RTK, Kerre EE (2007) Fuzzy audio similarity measures based on spectrum histogram and fluctuation patterns. In: Proceedings of the international conference multimedia and ubiquitous engineering 2007, Seoul, Korea
Malcangi M (2002) Soft-computing approach to fit a speech recognition system on a single-chip. In: Proceedings of 2002 international workshop system-on-chip for real-time applications, Banff, Canada
Malcangi M (2004) Improving speech endpoint detection using fuzzy logic-based methodologies. In: Proceedings of the thirteenth Turkish symposium on artificial intelligence and neural networks, Izmir, Turkey
O’Shaugnessy D (1987) Speech communication–human and machine. Addison-Wesley, Reading
Pak-Sum HH, Meng HM, Mak M (2007) Adaptive weight estimation in multi-biometric verification using fuzzy logic decision fusion. In: Proceedings of IEEE international conference on acoustic, speech, and signal processing
Runkler TA (1997) Selection of appropriate defuzzification methods using application specific properties. IEEE Trans Fuzzy Syst 5(1):72–79
Wahab A, Ng GS, Dickiyanto R (2005) Speaker authentication system using soft computing approaches. Neurocomputing 68:13–17
Jain AK, Dass SC, Nandakumar K (2004) Soft biometric traits for personal recognition systems. In: Proceedings of the international conference on biometric authentication, LNCS 3072, Hong Kong, pp 731–738
Jain AK, Nandakumar K, Lu X, Park U (2004) Integrating faces, fingerprint, and soft biometric traits for user recognition. In: Proceedings of biometric authentication workshop, LNCS 3087, Prague, pp 731–738
Hong A, Jain S, Pankanti S (1999) Can multibiometrics improve performance? In: Proceedings of IEEE workshop on automatic identification of advanced technologies
Malcangi M (2009) Robust speaker authentication based on combined speech and voiceprint recognition. In: Computational methods in science and engineering, ICCMSE 2008, American Institute of Physics CP 1148, vol 2
Hong L, Jain A, Sharathcha P, Bolle R (1996) Fingerprint enhancement. In: Proceedings 1st IEEE WACV, Sarasota, pp 202–207
Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–807
Devireddy SK, Siva K, Nagireddy D, Ramaswamy G, Ravikiran D, Sireesha P, Suresh Y, Babu Y (2008) A study of unimodel multimodel and soft biometric recognition. IJCSNS Int J Comp Sci Net Sec 8(8):36–46
Hong L, Jain A (1998) Integrating faces and fingerprints for personal identification. IEEE Trans Pattern Anal Mach Intell 20(12):1295–1307
Rabiner LR, Sambur MR (1975) An algorithm for determining the endpoints of isolated utterances. Bell Syst Tech J 54(2):297–315
Lamel L, Rabiner L, Rosenberg A, Wilpon J (1981) An improved endpoint detector for isolated word recognition. IEEE Trans Acoustics Speech Signal Proc 29(4):777–785
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Malcangi, M. Soft-computing methods for robust authentication using soft-biometric data. Neural Comput & Applic 20, 865–877 (2011). https://doi.org/10.1007/s00521-010-0514-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-010-0514-1