Incremental GMMSD2 with applications to feature extraction

The generalized MMSD (GMMSD) is considered an efficient implementation of MMSD to extract discriminative information. However, a significant issue with the implementation of GMMSD is the complete recomputation of the training process when new training samples are presented. In this paper, we propose an alternative solution for feature extraction using the principles of GMMSD, which we call GMMSD2. GMMSD2 only requires the computation of centroid matrix, and it can overcome computational cost by applying efficient QR-updating techniques when new training samples are presented. Our experiments on FERET database demonstrate that incremental version of GMMSD2 eliminates the complete recomputation of the training process when new training samples are available, leading to significantly reduced computational cost.