Class-label Locally Linear Embedding in Face Recognition

Abstract

Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has spurred increased interest in face recognition research recently. However, it is commonly known that a supervised method that considering the class-specific information always outperforms the unsupervised one, especially in biometric recognition task. In this paper, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE). cLLE aims to  discover the nonlinearity of high-dimensional data by minimizing the global reconstruction error of the set of all local neighbours in the data set. cLLE method is using user class-specific information in neighbourhoods selection and thus preserves the local neighbourhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases demonstrate the success of the proposed technique.