Fusion operators for multi-modal biometric authentication based on physiological signals

The most basic operators, like the sum and the product, have been used for data fusion in many application fields together with ordinal operators and the majority voting operator since the early stages of research. These application fields include biometrics, which constitutes the focus of the paper presented herein. All these operators have evolved into more advanced ones, particularly through the results of soft-computing and fuzzy operator research. However, these advances in state of the art have not been transfered to the different application fields. The presented work provides a comparison of different soft data fusion operators in a biometric application. Hence we analyze the performance of their application in a multimodal system, which takes into account two modalities based on physiological signals, electroencephalogram (EEG) and electrocardiogram (ECG). The analysis is done by evaluating the performance of five operators on a 29 subject database. The performance improvement due to the application of a soft data fusion stage is evaluated and demonstrated.

[1]  Andrey V. Bogdanov.  Neuroinspired Architecture for Robust Classifier Fusion of Multisensor Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Ludmila I. Kuncheva,et al.  "Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting , 2003, IEEE Trans. Fuzzy Syst..

[3]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[4]  I. Rudas.  New types of aggregation operators in intelligent systems: absorbing norms and evolutionary operators , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[5]  Nazzareno Pierdicca,et al.  Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies , 2006 .

[6]  E. Thorp,et al.  Best possible triangle inequalities for statistical metric spaces , 1960 .

[7]  K. Menger.  Statistical Metrics. , 1942, Proceedings of the National Academy of Sciences of the United States of America.

[8]  R. Mesiar,et al.  Triangular norms - basic properties and representation theorems , 2000 .

[9]  Bernard De Baets,et al.  Van Melle's combining function in MYCIN is a representable uninorm: An alternative proof , 1999, Fuzzy Sets Syst..

[10]  Giulio Ruffini,et al.  STARFAST : a Wireless Wearable EEG / ECG Biometric System based on the ENOBIO Sensor , 2008 .

[11]  Josef Kittler,et al.  Modified product fusion , 2002, Pattern Recognit. Lett..

[12]  Arun Ross,et al.  Multimodal biometrics: An overview , 2004, 2004 12th European Signal Processing Conference.

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  P. Bullen.  Handbook of means and their inequalities , 1987 .

[15]  Ronald R. Yager,et al.  Uninorm aggregation operators , 1996, Fuzzy Sets Syst..

[16]  Kar-Ann Toh,et al.  Maximizing area under ROC curve for biometric scores fusion , 2008, Pattern Recognit..

[17]  Aureli Soria-Frisch.  Soft data fusion for computer vision , 2004 .

[18]  Sukhendu Das,et al.  Enhancing decision combination of face and fingerprint by exploitation of individual classifier space: An approach to multi-modal biometry , 2008, Pattern Recognit..

[19]  Andrew Starr,et al.  A Review of data fusion models and architectures: towards engineering guidelines , 2005, Neural Computing & Applications.

[20]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[21]  L. Hong,et al.  Can multibiometrics improve performance , 1999 .

[22]  Wei-Wen Feng,et al.  Relevance assignment and fusion of multiple learning methods applied to remote sensing image analysis , 2006, 2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06).

[23]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Björn Waske,et al.  Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Imre J. Rudas,et al.  Information Aggregation in Intelligent Systems Using Generalized Operators , 2006, Int. J. Comput. Commun. Control.

[26]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[27]  Francesco Tortorella.  Special issue on "ROC Analysis in Pattern Recognition" , 2006, Pattern Recognit. Lett..