Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, we propose a novel approach based on Local Ternary Patterns (LTP) and the Multi-Level (ML) representation. Also, we investigate the effect of ML and Multi-Block (MB) representation for facial kinship verification , and the effect of different features representation. Moreover, the use of Fisher Score to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of six stages which are : (i) face preprocessing, (ii) features extraction, (iii) face representation (iv) pair features representation and normalization, (v) features selection and (vi) classification. The proposed approach is tested and analyzed on three publicly available databases (Cornell KinFace, UB Kin database, Familly 101, KinFac W-I and W-II). The obtained results are good comparable with the state-of-art approaches.