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 matrix to sense and transmit fingerprint images. The performance of the CS system was studied in detail by varying security parameters. sampling sparse coefficient are parameters considered from sensing, whereas encryption key is scheme. simultaneous compression has been achieved multiplying modeled data with proposed partial bounded orthogonal matrix. chaotic model-based permutation applied scramble DCT rows build Recovering decryption image accomplished help L1 optimization method. experimental test shows that vector 121 widths recovered taking about 25 samples. This indicates up 1 : 5 ratio supported without damaging minutiae. If only required encryption, 16 can be achieved. peak signal-to-noise (PSNR) 27.65 dB for both ratios under fulfilments all necessary requirements. 7.20 value entropy, histogram analysis, correlation analysis show scheme possesses adequate randomness. Furthermore, ability resistance against attacks proved 100% NPCR (Net Pixel Change Rate) 0.92% UACI (Unified Average Changing Intensity) values.