Face and Automatic Target Recognition Based on Super-Resolved Discriminant Subspace

Recently, super-resolution reconstruction (SRR) method of low-dimensional face subspaces has been proposed for face recognition. This face subspace, also known as eigenface, is extracted using principal component analysis (PCA). One of the disadvantages of the reconstructed features obtained from the super-resolution face subspace is that no class information is included. To remedy the mentioned problem, at first, this chapter will be discussed about two novel methods for super-resolution reconstruction of discriminative features, i.e., class-specific and discriminant analysis of principal components; that aims on improving the discriminant power of the recognition systems. Next, we discuss about twodimensional principal component analysis (2DPCA), also refered to as image PCA. We suggest new reconstruction algorithm based on the replacement of PCA with 2DPCA in extracting super-resolution subspace for face and automatic target recognition. Our experimental results on Yale and ORL face databases are very encouraging. Furthermore, the performance of our proposed approach on the MSTAR database is also tested. In general, the fidelity of data, feature extraction, discriminant analysis, and classification rule are four basic elements in face and target recognition systems. One of the efficacies of recognition systems could be improved by enhancing the fidelity of the noisy, blurred, and undersampled images that are captured by the surveillance imagers. Regarding to the fidelity of data, when the resolution of the captured image is too small, the quality of the detail information becomes too limited, leading to severely poor decisions in most of the existing recognition systems. Having used super-resolution reconstruction algorithms (Park et al., 2003), it is fortunately to learn that a high-resolution (HR) image can be reconstructed from an undersampled image sequence obtained from the original scene with pixel displacements among images. This HR image is then used to input to the recognition system in order to improve the recognition performance. In fact, super-resolution can be considered as the numerical and regularization study of the ill-conditioned large scale problem given to describe the relationship between low-resolution (LR) and HR pixels (Nguyen et al., 2001). On the one hand, feature extraction aims at reducing the dimensionality of face or target image so that the extracted feature is as representative as possible. On the other hand, super-resolution aims at visually increasing the dimensionality of face or target image. Having applied super-resolution methods at pixel domain (Lin et al., 2005; Wagner et al., 2004), the performance of face and target recognition applicably increases. However, with the emphases on improving computational complexity and robustness to registration error