Feature-based super-resolution for face recognition

In video surveillance, face images that are captured by common cameras usually have a low resolution, which is a great obstacle to face recognition. In this paper, we show that classification accuracy drops very quickly and algebraic features of face image change greatly as resolution decreases, and we further indicate that two orthogonal matrices (TOMs) of SVD contain the leading information for recognition. Based on the phenomena, we propose a feature-based face super-resolution method using eigentransformation by 2DPCA. TOMs of SVD on face image matrices are reconstructed instead of super-resolution being performed in pixel domain. Reconstructed algebraic features are formed into high-resolution face images and also be used for face recognition directly. Experiments demonstrate that our method obtains hallucinated face images with good vision, and improves performance of low-resolution face recognition.