Directional Multimode Subspace Analysis with Tensor Representation-Discriminant Feature Extraction

In this paper, we present a novel approach, namely directional multi-mode discriminant analysis which solves the supervised feature extraction problem by encoding the input image or high dimensional data array as a general tensor, and apply this for face and palm recognition. In the proposed scheme, the mode-k matrix of the tensor is re-sampled and re-arranged to form a mode-k directional image to better exploit the local structure information in training stage. An algorithm called mode-k direction linear discriminant analysis (LDA) is then presented to learn the multiple interrelated lower-dimensional subspaces without iterative step. Compared with conventional and other subspace analysis algorithms, the proposed method can greatly alleviate the small sample size problem, avoid the curse of dimensionality, reduce the computational cost in the learning stage by representing the data in lower dimension, simultaneously exploit the local structural information embedded in the high dimensional dataset, and obtain the better multiple low-dimensional subspace without iterative step as in existing tensor discriminant analysis. Experimental results on well-known face and UMIST databases show that the proposed method has higher recognition accuracy than many traditional subspace learning algorithms and tensor FLD scheme while using a low dimension of features.