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In this paper, weextensively investigate symmetrical two-dimensional principal component analysis (S2DPCA) and introduce two image measures for S2DPCA-based face recognition, volume measure (VM) and subspace distance measure (SM). Although symmetrical featuresare an obviously but not absolutely facial characteristic, they have been successfully applied to PCA and 2DPCA. The paper gives detailed evidence that even and odd subspaces in S2DPCA are mutually orthogonal, and particularly that S2DPCA can be constructed using a quarter of the conventional S2DPCA even/odd covariance matrix. Based on these theories, we investigate the time and memory complexities of S2PDCA further, and find that S2DPCA can in fact be computed using a quarter of the time and memory compared to conventional S2DPCA. Finally, VM and SM are introduced to S2DPCA for final classification. Our experiments compare S2DPCA with 2DPCA on YALE, AR and FERET face databases, and the results indicate that S2DPCA+VM generally outperforms other algorithms.