A Low Complexity Face Recognition Scheme Based on Down Sampled Local Binary Patterns

The accurate description of face images under variable illumination, pose and face expression conditions is a topic that has attracted the attention of researchers in recent years, resulting in the proposal of several efficient algorithms. Among these algorithms, Local Binary Pattern (LBP)-based schemes appear to be promising approaches, although the computational complexity of LPB-based approaches may limit their implementation in devices with limited computational power. Hence, this paper presents a face recognition algorithm, based on the LBP feature extraction method, with a lower computational complexity than the conventional LBP-based scheme and similar recognition performance. The proposed scheme, called Decimated Image Window Binary Pattern (DI-WBP), firstly, the face image is down sampled and then the LBP is applied to characterize the size reduced image using non overlaping blocks of 3x3 pixels. The DI-WBP does not require any dimensionality reduction scheme because the size of the resulting feature matrix is much smaller than the original image size. Finally, the resulting feature vectors are applied to a given classification method to perform the recognition task. Evaluation results using the Aleix-Robert (AR) and Yale face databases demonstrate that the proposed scheme provides a recognition performance similar to those provided by the conventional LBP-based scheme and other recently proposed approaches, with lower computational complexity