DCT based region log-tiedrank covariance matrices for face recognition

Gabor-based region covariance matrix (GRCM) has been demonstrated as a promising descriptor for face recognition. However, GRCM requires large number of filters to achieve satisfactory performance. Furthermore, complex-valued Gabor filters require double convolution operations for each filter that makes the computation more expensive. To alleviate the problem, we propose to adopt real-valued discrete cosine transform (DCT) as filter bank in place of complex-valued Gabor filter. DCT as an orthogonal transform however decorrelates the signal, leads to most energies fall into the diagonal entries of the constructed covariance matrix, which is ill-formed for RCM. We demonstrate that applying non-linear operation on the DCT filter responses ameliorates the decorrelated filter responses effects. Apart from that, while RCM offers spatial information that is useful for recognition tasks, overly small RCM region renders poor covariance estimation, which can affect the recognition performance drastically. In this paper we also propose Log-TiedRank to mitigate the potential undersampling effect suffered by covariance matrix estimation. From the experiments Log-TiedRank shows surprising performance boost over AIRM and Log-Euclidean metric especially when both gallery set and probe set have very different distributions.