A comparative study of biohashing technique using different feature extraction methods has been carried out. A basic
biohashing technique involves two steps. First is the extraction of the features from the biometrics and second is the
discretization of the obtained features with the random numbers. In this paper, two different data bases and two different
feature extraction techniques have been studied. The first data base consists of self generated images and images from
ORL and Indian face database and the second data base is taken from the standard Yale-B data base. The two different
feature extraction techniques that have been used are the optics based joint transform correlator technique and the
principle component analysis technique. Hamming distance has been used to discriminate the different populations. The
performance of the techniques has been analyzed by using the false rejection ratio (FRR), false acceptance ratio (FAR)
and the equal error rate (EER) in order to compare the effect of the different feature extraction methods on the
biohashing technique.
We propose a new method for image encryption and decryption in which the image is broken up into bit planes. Each bit plane undergoes a jigsaw transform. The transformed bit planes are combined together and then encrypted using random phase masks and fractional Fourier transforms. The different fractional parameters, the random phase codes, and the jigsaw transform index form the key to the encrypted data. This increases the robustness of the encryption system by several orders of magnitude. Different variations of the juxtaposition of the pieces of the image are also considered. These include the rotated version of the jigsaw pieces. The computational complexity of the bit-plane-based jigsaw algorithm is further improved using the third dimension (i.e., along different bit planes) for scrambling as well. The results of computer simulation are presented to verify the proposed idea and analyze the performance of the proposed techniques.
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