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In this paper we propose a novel method for face recognition on frontal faces. In our method the coarse level shape information of the face is captured by Radon transform as it captures the shape information efficiently. A feature descriptor for each face is computed by applying local binary patterns (LBP) on Radon transform coefficients and computing the histogram of LBP. LBP is used due its computational simplicity and its good texture analysis capabilities. Individual histograms computed on each sub-block of the face image are concatenated in spatial pyramid fashion to attain the complete descriptor. These face descriptors are matched using a distance measure based on pyramid matching kernel(PMK). We evaluated this method using various distance metrics. Experimental results on FERET database shows the significance of this method.

Digital watermarking is a process to provide authenticity by hiding a data into an image or audio or document. Hiding of data in an image can be done in frequency domain. Since frequency domain based techniques are more robust against signal processing and geometric attacks than time domain techniques. One such frequency domain technique is developed based on combination of discrete wavelet transform, singular value decomposition and torus automorphism and is presented in this paper. This results prove that this method robust against different attacks and has good PSNR ratio.

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