Side-view face authentication based on wavelet and random forest with subsets

This paper provides a novel side-view face authentication method based on discrete wavelet transform and random forest. A subset selection method that increases the number of training samples and allows subsets to preserve the global information is presented. The authentication method can be summarized to have the following steps: profile extraction, wavelet decomposition, subset splitting and random forest verification. The new method takes the advantage of wavelet's localization property in both frequency and spatial domains, while maintaining the generalized properties of random forest. The implementation of the proposed method is computationally feasible and the experimental results show that the performance is satisfactory. Future improvements are discussed in the paper.