A Novel Adaptive Two-phase Multimodal
Biometric Recognition System
Venkatramaphanikumar Sistla1,
Venkata Krishna Kishore Kolli1, and Kamakshi Prasad Valurouthu2
1Department of Computer Science and Engineering, Vignan’s
Foundation for Science, Technology and Research, India
2Department
of Computer Science and Engineering, Jawaharlal Nehru Technological University
Hyderabad College of Engineering, India
Abstract: Multimodal biometric recognition systems are
intended to offer authentication without compromising on security, accuracy and
these systems also used to address the limitations of unimodal systems like
spoofing, intra class variations, noise and non-universality. In this paper, a
novel adaptive two-phase multimodal framework is proposed with face, finger and
speech traits. In this work, face trait reduces the search space by retrieving
few possible nearest enrolled candidates to the probe using Gabor wavelets,
semi-supervised kernel discriminant analysis and two dimensional- dynamic time
warping. This nonlinear face classification serves as a search space reducer and
affects the True Acceptance Rate (TAR). Later, level-1 and level-2 features of
fingerprint trait are fused with Dempster Shafer theory and achieved high TAR.
In the second phase, to reduce FAR and to validate the user identity, a text
dependent speaker verification with RBFNN classifier is proposed. Classification
accuracy of the proposed method is evaluated on own and standard datasets and
experimental results clearly evident that proposed technique outperforms existing
techniques in terms of search time, space and accuracy.
Keywords: Gabor filters, radial basis function,
discrete wavelet transform, dynamic time warping kernel discriminant analysis.
Received April 18, 2017; accepted June 13, 2017