  In this paper, we proposed an effective face feature extraction method by
Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component
Analysis (WPCA), called LGLG-WPCA. The proposed method learns face features
from the embedded multivariate Gaussian in Gabor wavelet domain; it has the
robust performance to adverse conditions such as varying poses, skin aging and
uneven illumination. Because the space of Gaussian is a Riemannian manifold and
it is difficult to incorporate learning mechanism in the model. To address this
issue, we use L2EMG to map the multidimensional Gaussian model to the linear
space, and then use WPCA to learn face features. We also implemented the
key-point-based version of LGLG-WPCA, called LGLG(KP)-WPCA. Experiments show
the proposed methods are effective and promising for face texture feature
extraction and the combination of the feature of the proposed methods and the
features of Deep Convolutional Network (DCNN) achieved the best recognition
accuracies on FERET database compared to the state-of-the-art methods. In the
next version of this paper, we will test the performance of the proposed
methods on the large-varying pose databases.
