A High and Efficient Sparse and Compressed Sensing-Based Security Approach for Biometric Protection

We propose a highly efficient sparse code with compressive sensing security algorithm based on the Dual-tree Complex Wavelet Transform (DT-CWT) and Hadamard measurement matrix in this paper for biometric protection. Firstly, we use DT-CWT to translate the image into frequency domain and use chaotic systems to encrypt measurement matrices. Also noise shaping is employed in the DT-CWT coefficients to represent the image sparsely. Then, we use compression sensing algorithm to improve the compression rate of encrypted images, and reduce the storage space occupied by images. Finally, in order to improve the algorithm’s capability of handle contaminated images, we use the robustness of the double random phase encoding based on 4f optics system algorithm as secondary encryption. In the image decryption, we use the OMP algorithm. Finally, we can see that our proposed algorithm achieves 37.9863 dB in PSNR, 0.0245 in ERROR and 0.9977 in NC.