In recent years, face recognition has achieved high accuracy and has been widely used in many fields. However, the performance of face recognition is not satisfied in some unrestricted situations such as in monitoring scenarios. The light field camera, as an image acquisition equipment, can capture images before focusing them and allows us to achieve clearer images. However, the existing face recognition algorithms based on light field camera need to search the optimal face manually. In this paper, an automatic algorithm for optimal face search is proposed. With this search algorithm, a new face recognition framework based on light field camera is proposed. Two face recognition algorithms are used to test the proposed framework. The experimental results show that the proposed methods combined with the light field camera can achieve better results than with conventional camera and the whole framework does not need extra manual operation.
Watermarking is of vital importance for copyright protection and content authentication of images. With the development of compressive sensing, it has been successfully applied for watermarking with improved performance. Since an image can exhibit tree structure in wavelet domain, a new watermarking embedding and extraction method is proposed based on tree-structured Bayesian compressive sensing. The Markov Chain Monte Carlo (MCMC) method and the variational Bayesian (VB) analysis can be used for inference, respectively. Attacks to the watermarking, such as Gaussian noise, salt and pepper noise, Gaussian filtering, and JPEG compression, are given to evaluate the watermarking robustness with comparison to other reported reconstruction algorithms such as basis pursuit, orthogonal matching pursuit, Bayesian compressive sensing using relevance vector machine (RVM), and Bayesian compressive sensing with VB. Simulation results and comparisons show remarkable advantages of the tree-structured Bayesian compressive sensing for watermarking embedding and extraction.