Face verification using LBP feature and clustering

In this paper, we present a mechanism to extract certain special faces—LBP-Faces, which are designed to represent different kinds of faces around the world, and utilize them as the basis to verify other faces. In particular, we show how our idea can integrate with Local Binary Pattern (LBP) and improve its performance. Other than most of the previous LBP-variant approaches, which, no matter try to improve coding mechanism or optimize the neighbourhood sizes, first divide a face into patch-level regions (e.g. 7×7 patches), concatenating histograms calculated in each patch to derive a rather long dimension vector, and then apply PCA to implement dimension reduction, our work use original LBP histograms, trying to retain the major properties such as discriminability and invariance, but in a much bigger component-level region (we divide faces into 7 components). In each component, we cluster LBP descriptors—in the form of histograms to derive N clustering centroids, which we define as LBP-Faces. Then, to any input face, we calculate its similarities with all these N LBP-Faces and use the similarities as final features to verify the face. It looks like we project the faces image into a new feature space—LBP-Faces space. The intuition within it is that when we depict an unknown face, we are prone to use description such as how likely the face´s eye or nose is to an known one. Result of our experiment on the Labeled Face in Wild (LFW) database shows that our method outperforms LBP in face verification.