Fusion of correlated decisions for writer verification

Abstract A fusion approach is proposed for improving the efficiency of writer verification systems. A short handwritten sentence is employed for this purpose. Each word of the sentence is used to tackle an individual verification problem. Then, the word-level (local) decisions are fused in order to obtain a more reliable global decision by means of the Neyman–Pearson approach. The correlation of the local decisions is extensively studied and incorporated in the fusion procedure by means of the Bahadur–Lazarsfeld expansion series. A database containing 4800 sentences is employed to validate the performance of the method. The improvement in verification performance is due to both the fusion procedure applied and the full discretion of the writer to choose his own secret word.

[1]  Pramod K. Varshney.  Multisensor data fusion , 1997 .

[2]  Keinosuke Fukunaga,et al.  Statistical Pattern Recognition , 1993, Handbook of Pattern Recognition and Computer Vision.

[3]  V. Anastassopoulos,et al.  Fusion of correlated decisions for writer identification , 1998 .

[4]  V. Aalo,et al.  On distributed detection with correlated sensors: two examples , 1989 .

[5]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[6]  Vassilis Anastassopoulos,et al.  Contour features using morphological transforms , 1998 .

[7]  Michael Fairhurst,et al.  Signature verification revisited: promoting practical exploitation of biometric technology , 1997 .

[8]  Ramanarayanan Viswanathan,et al.  Optimal Decision Fusion in Multiple Sensor Systems , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Teruo Fukumura,et al.  Off-Line Preprocessing and Verification of Signatures , 1988, Int. J. Pattern Recognit. Artif. Intell..

[10]  Robert Sabourin,et al.  A neural network approach to off-line signature verification using directional PDF , 1996, Pattern Recognit..

[11]  W. Gray,et al.  Optimal data fusion of correlated local decisions in multiple sensor detection systems , 1992 .

[12]  F. Prêteux,et al.  Off-line signature verification by local granulometric size distributions , 1997 .

[13]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[14]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[15]  E. Drakopoulos,et al.  Optimum multisensor fusion of correlated local decisions , 1991 .

[16]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[17]  Yingyong Qi,et al.  Signature verification using global and grid features , 1994, Pattern Recognit..

[18]  Réjean Plamondon,et al.  Automatic Signature Verification: The State of the Art - 1989-1993 , 1994, Int. J. Pattern Recognit. Artif. Intell..

[19]  John D. Woodward,et al.  Biometrics: privacy's foe or privacy's friend? , 1997, Proc. IEEE.

[20]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.