Image-Set Matching by Two Dimensional Generalized Mutual Subspace Method

In this paper, we present a novel supervised learning algorithm for object recognition from sets of images, where the sets describe most of the variation in an object's appearance caused by lighting, pose and view angle. In this scenario, generalized mutual subspace method (gMSM) has attracted attention for image-set matching due to its advantages in accuracy and robustness. However, gMSM employs PCA, which has high computational cost contrasting to state-of-art appearance-based methods. To create a faster method, we replace the traditional PCA by 2D-PCA and variants on gMSM framework. In general, 2D-PCA and variants require less memory resource than conventional PCA since its covariance matrix is calculated directly from two-dimensional matrices. The introduced method has the advantage of representing the subspaces in a more compact manner, providing reasonably competitive recognition rate comparing to the traditional MSM, confirming the suitability of employing 2D-PCA and variants on gMSM framework. These results have been revealed through experimentation conducted on five widely used datasets.