The existing kinship recognition methods can only recognize single-parent relationships(eg. father-son, father-daughter, mother-son, mother-daughter). However, these methods have certain limitations and are not effective in recognizing samples of parents with large age gaps and different genders. In order to solve the above problems, this paper proposes a method of identifying kinship(based on parental relationship). As there are few methods for recognizing the parental relationship, this paper proposes a kinship model for recognizing parental relationship based on metric learning and correlation analysis to improve the accuracy of recognizing kinship. First, based on the DNA of children and parents, a multi-linear parallel network of integrating the kinship characteristics of children and parents is designed. Then, using the advantages of discriminative metric learning and canonical correlation analysis in data processing, the facial features benefitting kinship recognition are extracted from multiple human identities, which are used to identify whether there is a relationship between the children and their parents to improve the accuracy. Experimental results show that the proposed method has a better effect on the recognition of the kinship between children and there's parents.