Abstract
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.
Highlights
Face recognition is becoming a very popular subject for research
It can be seen that S2DPCA+volume measure (VM) generally outperforms others
For the purpose of some applications, e.g., verification from biometric signatures stored on smart cards [28], we validate S2DPCA on the FERET face database [29], which has been widely used to evaluate face recognition approaches
Summary
Face recognition is becoming a very popular subject for research. It can be applied in many fields[1], including mobile robots, information security, and entertainment. The row or column of the 2D face image has a low dimensionality, so the covariance matrix in 2DPCA is more precise than thatin PCA This is the key to solving the impact of the “dimensionality curse” problem in 2DPCA‐based face recognition[4,8]. Symmetrical principal component analysis (SPCA) [19], an approach obtained by the even‐odd decomposition principle, expands samples from the facial symmetry [19,20] In essence, this approach improves recognition performance by exploiting priorknowledge, i.e., the symmetrical characters of the face image. Good performance has been obtained by SKPCA [21].these approaches are all based on 1D vectorized face images.Recently, a new facial symmetry approach called symmetry two‐ dimensional PCA (S2DPCA), has been proposed [22]. The last section concludes this paper and discusses future work
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