This study presents a new adaptive data-hiding method based on least-significant-bit (LSB) substitution and pixel-value differencing (PVD) for grey-scale images. The proposed method partition the cover image into some non-overlapping blocks having three consecutive pixels and select the second pixel of each block as the central pixel (called base-pixel). Then, k-bits of secret data are embedded in the base pixel by using LSB substitution and optimal pixel adjustment process (OPAP). The difference between the base-pixel value and other pixel values in the block are utilised to determine how many secret bits can be embedded in the two pixels. Also, the method divides all possible differences into lower level and higher level with a number of ranges. Then, it obtains the number of the secret bits that will be embedded into each block depending on the range which the difference values belong to. The experimental results show that the proposed method can embed a large amount of secret data while maintaining a high visual quality of the stego-images. The peak signal-to-noise ratio (PSNR) values and the embedding capacity of our method are higher than those of three other data-hiding methods which are investigated in this study.

This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.