Orthogonal multilinear discriminant analysis and its subblock tensor analysis version

This paper introduces an orthogonal multilinear discriminant analysis (OMDA) algorithm for gait recognition. The discriminant feature vectors of OMDA are orthogonal to each other. With the advantage of extracting a portion of local information and reducing computational complexity, the subblock tensor analysis is employed to OMDA, named subblock orthogonal multilinear discriminant analysis (SOMDA). Considering that the vectors from different subblocks have different contributions to recognition, these vectors are given different weights and synthesized into a whole vector in the recognition process. We have conducted a comparative study on gait recognition to evaluate OMDA and SOMDA in terms of classification. With the tensor vectorization methods according to both variance and class discriminability, the OMDA-based recognition algorithm indicates that it outperforms other multilinear subspace solutions such as MPCA, MPCA + LDA, GTDA, DATER and UMDA. In the subblock experiments, it indicates that SOMDA is an improvement over OMDA.