Face recognition using average invariant factor

The recent developed intrinsic discriminate analysis (IDA) demonstrates superior recognition rate compared with classical methods such as PCA and LDA. In this paper, we not only re-prove the core theorem of IDA from a new perspective, but also define the Average Invariant Factor (AIF) that generalizes IDA. Two new algorithms for face recognition are built upon the AIF by using SVD and QR decomposition. Moreover, this new formulation facilitates the kernel extensions for the recognition algorithms, which relax the linear assumption for IDA. The presented kernel based AIF algorithms also significantly lower down the computational expenses of the original IDA method. A series of experiments on YALE and ORL sets demonstrate higher performance in terms of recognition rate and efficiency compared with classical statistical analysis methods (e.g., PCA, KPCA and 2DPCA) and the IDA algorithm.