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.
|Title of host publication||2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2016 May 18|
|Event||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China|
Duration: 2016 Mar 20 → 2016 Mar 25
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016|
|Period||16/3/20 → 16/3/25|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering