Neighbourhood Discriminant Locally Linear Embedding in Face Recognition

Face images are often very high-dimensional and complex. However, the actual underlying structure can be characterized by a small number of features. Hence, locally linear embedding (LLE) is proposed as a nonlinear dimension reduction technique to deal this problem. LLE learns the intrinsic manifold embedded in the high dimensional ambient space by minimizing the global reconstruction error of the neighbourhood in the data set. LLE is popular in analyzing face images with different poses, illuminations or facial expressions for one subject class. It is developed based on the assumption that data that is distributed on a single manifold is having the same class label; hence the process of neighborhood selection is non class-specific. However, this is inappropriate to face recognition as face recognition learns in multiple manifolds where each representing data on one specific class. Here, we modify the original LLE by embedding prior class information in the process of neighborhood selection. Experimental results demonstrate that our technique consistently outperforms the original LLE in ORL, PIE and FRGC databases.