Face recognition based on random feature

This paper presents a simple, yet very efficient facial image representation based on random feature. We describe the face on three different levels of locality. Firstly, random features are extracted from local image patches with random projection and then we use BoW model to get labels on a pixel-level by coding the random features to the closest textons. Secondly, the labels are statistically calculated over a region to get histogram containing information on a regional level. Thirdly, the regional histograms are concatenated to build a global description of the face. Experiments conducted on the FERET database show that our approach has outstanding robustness to variations, especially to noise.