This paper describes a method of discriminant analysis for cross-view recognition with a relatively small number of training samples. Since appearance of a recognition target (e.g., face, gait, gesture, and action) is in general drastically changes as an observation view changes, we introduce multiple view-specific projection matrices and consider to project a recognition target from a certain view by a corresponding view-specific projection matrix into a common discriminant subspace. Moreover, conventional vectorized representation of an originally higher-order tensor object (e.g., a spatio-temporal image in gait recognition) often suffers from the curse of dimensionality dilemma, we therefore encapsulate the multiple view-specific projection matrices in a framework of discriminant analysis with tensor representation, which enables us to overcome the curse of dimensionality dilemma. Experiments of cross-view gait recognition with two publicly available gait databases show the effectiveness of the proposed method in case where a training sample size is small.
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