Enhanced locality sensitive discriminant analysis for image recognition

An improved manifold learning method, called enhanced locality sensitive discriminant analysis (ELSDA), for image recognition is proposed. Motivated by the fact that statistically uncorrelated and parameter-free are two desirable and promising characteristics for feature extraction, a new difference-based optimisation objective function with uncorrelated constraint for appearance-based image recognition has been designed. Experimental results demonstrate the efficacy of the proposed method.