  This paper proposes a multimodal biometric system through Gaussian Mixture
Model (GMM) for face and ear biometrics with belief fusion of the estimated
scores characterized by Gabor responses and the proposed fusion is accomplished
by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with
Gabor wavelet filters to extracts spatially enhanced Gabor facial features and
Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face
and Gabor ear responses separately for quantitive measurements. Expectation
Maximization (EM) algorithm is used to estimate density parameters in GMM. This
produces two sets of feature vectors which are then fused using Dempster-Shafer
theory. Experiments are conducted on multimodal database containing face and
ear images of 400 individuals. It is found that use of Gabor wavelet filters
along with GMM and DS theory can provide robust and efficient multimodal fusion
strategy.
