Face recogntion in open world environment

Face recognition in open world environment is a very challenging task due to variant appearances of the target persons and a large scale of unregistered probe faces. In this paper we combine two parallel classifiers, one based on the Local Binary Pattern (LBP) feature and the other based on the Gabor features, to build a specific face recognizer for each target person. Faces used for training are borderline patterns obtained through a morphing procedure combing target faces and random non-target ones. Grid-search is applied to find an optimal morphing-degree-pair. By using an AND operator to integrate the prediction of the two complementary parallel classifiers, many false positives are eliminated in the final results. The proposed algorithm is compared with the Robust Sparse Coding method, using selected celebrities as the target persons and the images from FERET as the non-target faces. Experimental results suggest that the proposed approach is better at tolerating the distortion of the target person's appearance and has a lower false alarm rate.