Randomized Canonical Correlation Discriminant Analysis for face recognition

As an important technique in multivariate statistical analysis, Canonical Correlation Analysis (CCA) has been widely used in face recognition. But existing CCA based face recognition methods need two kinds of expression for the same face sample, and usually suffers high computational complexity in dealing with large samples. In this paper, we present a supervised method called Randomized Canonical Correlation Discriminant Analysis (RCCDA) based on Randomized non-linear Canonical Correlation Analysis (RCCA) to make up for the shortage of CCA based face recognition methods. We first obtain basis vectors approximately with random features instead of the calculation of kernel matrix to improve the efficiency of computation, then we use these basis vectors to compute random optimal discriminant features which can reduce the dimension of face features while preserving as much discriminatory information as possible. The result of experiments on Extended Yale B, AR, ORL and FERET face databases demonstrates that the performance of our method compares favorably with some state-of-the-art algorithms.