A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-off coefficients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits
of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.
An efficient, new method for computing texture features based on dominant local orientation is introduced. The features are computed as the Laplacian pyramid is built up. At each level of the Laplacian pyramid, the linear symmetry feature is computed. This feature is anisotropic and estimates the optimal local orientation in the Least Square Error (LSE) sense. This corresponds to the orientation interpolation obtained from the filter responses of a polar gabor decomposition of the local image. The linear symmetry feature consists of two components, the local orientation estimate and its confidence measure based on the error. Since the latter is a measure of evidence for existence of a definite direction, the linear symmetry feature computed as proposed, has the property of orientation selectivity within each frequency channel. The algorithm is based on convolutions with simple separable filters and pixel-wise non-linear arithmetic operations. These properties allow highly parallel implementation, for example on a pyramid machine, yielding real time applications. Experiments based on test images of natural textures are presented.
Conference Committee Involvement (4)
Biometric Technology for Human Identification IV
9 April 2007 | Orlando, Florida, United States
Biometric Technology for Human Identification III
17 April 2006 | Orlando (Kissimmee), Florida, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.