Multiple feature fusion for face recognition

Recent studies show face recognition (FR) with additional features achieves better performance than that with single one. Different features can represent different characteristics of human faces, and utilizing different features effectively will have positive effect on FR. Meanwhile, the advances of sparse coding enable researchers to develop various recognition methods to cooperate with multiple features. However, even if these methods achieve very encouraging performances, there still exist some intrinsic problems. Firstly, these methods directly encode the multiple features over the original training set, by which way some redundant, noisy and trivial information are incorporated and the recognition performance can be compromised. Moreover, when the training data increase in number, the jointly-encoding process can be very time-consuming. Thirdly, these methods ignore some semantic relationships among the features, which can boost the FR performance. Thus, coarsely utilizing all the features not only adds extra computation burden, but also prevent further improvement. To address these issues, we propose to fuse the multiple features into a more preferable presentation, which is more compact and more discriminative for better FR performance. As well, we take advantage of the dictionary learning framework to derive an effective recognition scheme. We evaluate our model by comparing it with other state-of-the-art approaches, and the experimental results demonstrate the effectiveness of our approach.