Ear recognition based on uncorrelated local Fisher discriminant analysis

In this paper, we propose an improved manifold learning method, called uncorrelated local Fisher discriminant analysis (ULFDA), for ear recognition. Motivated by the fact that the features extracted by local Fisher discriminant analysis are statistically correlated, which may result in poor performance for recognition. The aim of ULFDA is to seek a feature submanifold such that the within-manifold scatter is minimized and between-manifold scatter is maximized simultaneously in the embedding space by using a new difference-based optimization objective function. Moreover, we impose an appropriate constraint to make the extracted features statistically uncorrelated. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and Spain, USTB-2 and CEID ear databases are performed to demonstrate the effectiveness of the proposed method.