Face Feature Extraction Based on Uncorrelated Locality Information Projection

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features. In this paper, a new manifold learning algorithm, called Uncorrelated Locality Information Projection (ULIP), to identify the underlying manifold structure of a data set. ULIP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently, the goal of ULIP is to preserve the intrinsic graph characterizes the interclass compactness and connects each data point with its neighboring points of the same class. Different from Principal component analysis(PCA)that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance and the optimal mapping is the leading eigenvectors of the total variance matrix associated with the leading eigenvalues, While locality preserving projections(LPP) that is in favor of preserving the local structure of the data set. we choose proper dimension of subspace that detects the intrinsic manifold structure for classification tasks. Extensive experiments on face recognition demonstrate that the new feature extractors are effective, stable and efficient.