The kinship Verification system analyzes the facial features of two people to determine whether they are related or not. To identify the kinship, different features can be extracted from the faces. In this paper, to evaluate a kinship verification system for the first-generation kinship (father-son, father-daughter, mother-son, and mother-daughter), texture and color features are tested, and feature fusion, as well as examining several different classifiers is considered. In this regard, two proposed approaches have been proposed: (1) fusing effective features and evaluate different classifiers for kinship verification and (2) using NRML metric learning to generate a distinctive feature vector to increase kinship verification efficiency. The proposed methods for the two databases KinFaceW-I and KinFaceW-II have been analyzed and evaluated in different cases. The results of the evaluations show that the fusion of features and the use of NRML metric learning have been able to improve the performance of the kinship verification system. In addition to the two proposed approaches, feature extraction from the whole image as well as image blocks is proposed and the results are presented. The results indicate that using the block-wise method for feature extraction can be effective in improving the final kinship verification results.