Two Feature-Level Fusion Methods with Feature Scaling and Hashing for Multimodal Biometrics

ABSTRACT

This paper presents a new feature-level information fusion mechanism based on shuffle coding, called shuffle coding-based feature-level fusion (SC-FLF), for personal authentication. Our approach (SC-FLF) aims at constructing an information fusion mechanism to integrate features from the same or different feature spaces in which the ranges of feature values from different traits differ largely. In this mechanism, the shuffle-coding operator includes dimension adjustment, feature standardization, and fusion coding. This paper addresses two distinct methods, such as feature scaling and hashing, to standardize the range of independent features of data. The shuffle encoder of the SC-FLF in Method 1 uses a feature scaling and the resulting binary code represents the distance between a set of normalized feature values with 2’s complement. On the other hand, in Method 2, the shuffle encoder of the SC-FLF with hashing uses a projection framework for maximizing the features on a hyperplane and then quantizes the hash values as a sequence of binary codes. A XOR operation works as the fusion coding to produce the resulting fusion code. Three different types of fusion are designed to evaluate the fusion performance. Experimental validation illustrates that the proposed fusion methods for combining features in multimodal biometrics advances the recognition performance significantly.

KEYWORDS:

• Authentication
• Biometrics
• Feature fusion
• Feature scaling
• Hashing
• Information fusion
• Multimodal biometric

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ren-He Jeng

Ren-He Jeng received the MS degree in Electrical Engineering in 2009 from the National Chi Nan University, Nantou, Taiwan, ROC., where he is currently pursuing the PhD degree in Electrical Engineering at the Department of Electrical Engineering. His research interests include image processing, pattern recognition, and biometrics.

E-mail: [email protected]

Wen-Shiung Chen

Wen-Shiung Chen received the MS degree from National Taiwan University, Taipei City, Taiwan, in 1985, and the PhD degree from the University of Southern California, Los Angeles, in 1994, both in Electrical Engineering. He was with the Department of Electrical Engineering, Feng Chia University, Taichung, Taiwan from 1994 to 2000. In 2000, he joined the Department of Electrical Engineering, National Chi Nan University, Puli, Nantou, Taiwan, where he is currently a Professor. His research interests include image processing, pattern recognition, computer vision, biometrics, mobile computing, and networking.

E-mail: [email protected]