Fast face hallucination with sparse representation for video surveillance

In this paper we propose a novel face hallucination algorithm to synthesize a high-resolution face image from several low-resolution input face images. As described in Liu et al. [8]'s work, face hallucination uses two models: a global parametric model which synthesizes global face shapes from eigenfaces, and a local parametric model which enhances the local high frequency details. We follow a similar process to develop a robust face hallucination algorithm. Firstly, we obtain eigenfaces from a number of low resolution face images extracted from a video sequence using a face tracking algorithm. Then we compute the difference between the interpolated low-resolution face and the mean face, and use this difference face as the query to retrieve approximate sparse eigenfaces representation. The eigenfaces are combined using the coefficients obtained from sparse representation and added into the interpolated low-resolution face. In this way, the global shape of the interpolated low resolution face can be successfully enhanced. Secondly, we improve the example-based super-resolution method [7] for local high frequency information enhancement. Our proposed algorithm uses the Approximate Nearest Neighbors (ANN) search method to find a number of nearest neighbors for a stack of queries, instead of finding the exact match for each low frequency patch as presented in [7]. Median filtering is used to remove the noise from the nearest neighbors in order to enhance the signal. Our proposed algorithm uses sparse representation and the ANN method to enhance both global face shape and local high frequency information while greatly improving the processing speed, as confirmed empirically.