This paper proposes a statistically optimum adaptive wavelet packet (WP) thresholding function for image denoising based on the generalized Gaussian distribution. It applies computationally efficient multilevel WP decomposition to noisy images to obtain the best tree or optimal wavelet basis, utilizing Shannon entropy. It selects an adaptive threshold value which is level and subband dependent based on analyzing the statistical parameters of subband coefficients. In the utilized thresholding function, which is based on a maximum a posteriori estimate, the modified version of dominant coefficients was estimated by optimal linear interpolation between each coefficient and the mean value of the corresponding subband. Experimental results, on several test images under different noise intensity conditions, show that the proposed algorithm, called OLI-Shrink, yields better peak signal noise ratio and superior visual image quality—measured by universal image quality index—compared to standard denoising methods, especially in the presence of high noise intensity. It also outperforms some of the best state-of-the-art wavelet-based denoising techniques.

One of the new challenges of biometric systems based on face analysis is kinship verification. Little efforts have been done in spite of the importance and functionality of this subject. Most of existing methods have been trying to exploit and represent techniques based on metric learning to increase verification rate, paying no attention to the effect of the features extracted from the faces. Despite the previous methods exploiting simple local features, we have focused on the combination and selection of effective features in this paper. To this end, local and global features were combined to describe the face images in a better way. The effective and discriminative features were selected using the kinship genetic algorithm and then fulfilled kinship verification. The proposed method is tested and analysed on the standard and big datasets KinFaceW-I and KinFaceW-II, and verification rates of 81.3% and 86.15% were obtained respectively.