Symmetrical feature extraction via novel Mirror PCA

Abstract Symmetry is an important property of human faces. Many researchers exploit symmetry to improve face recognition. In this paper, we explore the symmetry of faces under the framework of PCA (Principal Component Analysis). Unlike previous studies that manipulate facial symmetry by averaging the two halves, we argue that well-estimated symmetrical faces lie in a low-dimensional subspace. Inspired by this, a PCA-like model, referred to as Mirror PCA, is proposed to extract low-rank symmetrical features of faces, as well as their principal directions. Although the optimization problem is non-convex, a suboptimal solution could be found under existing optimization frameworks such as ADMM, PGD, etc. A number of experiments are conducted to verify the effectiveness and robustness of our method.