An Image Region Selection with Local Binary Pattern based for Face Recognition

摘要

In this paper, we present a novel framework for face recognition, namely Selective Ensemble of Image Regions (SEIR) is proposed and which considers both shape and texture information to represent face images. In this framework, all possible regions in the face image are regarded as a certain kind of features. This technique can be adapted to accurately detect facial features. However, the area of the image being analyzed for a facial feature needs to be regionalized to the location with the highest probability of containing the feature. By regionalizing the detection area, false positives are eliminated and the speed of detection is increased due to the reduction of the area examined. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbor classifier in the computed feature space. The FERET data tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction a method to accurately and rapidly detect faces within an image.