Compressed sensing based fingerprint imaging system using a chaotic model-based deterministic sensing matrix

A secured compressed sensing (CS) systems design approach uses a novel deterministic sensing matrix to sense and transmit fingerprint images. The performance of the CS system was studied in detail by varying CS and security parameters. The sampling and sparse coefficient are the parameters considered from compressed sensing, whereas the encryption key is from the security scheme. The simultaneous compression and encryption has been achieved by multiplying the sparse modeled data with the proposed deterministic partial bounded orthogonal sensing matrix. A chaotic model-based permutation is applied to scramble the DCT matrix rows to build the sensing matrix. Recovering and decryption of the compressed image are accomplished with the help of the L-1 optimization method. The experimental test shows that a sparse vector of 121 widths has been recovered by taking about 25 samples. This indicates that up to 1 : 5 compression ratio is supported without damaging the fingerprint minutiae. If only compression is required without encryption, up to a 1 : 16 ratio can be achieved. The peak signal-to-noise ratio (PSNR) is 27.65 dB for both compression ratios under fulfilments of all necessary security requirements. The 7.20 value of the entropy, histogram analysis, and the correlation analysis show the proposed scheme possesses adequate randomness. Furthermore, the ability of the system resistance against attacks is proved by 100% NPCR (Net Pixel Change Rate) and 0.92% UACI (Unified Average Changing Intensity) values.

Automated summary

A secure compressed sensing system design approach is proposed in this study, which uses a novel deterministic sensing matrix to sense and transmit fingerprint images. The experimental results show that the proposed method can achieve high compression ratios without damaging the fingerprint minutiae and possesses sufficient randomness and resistance against attacks.