A Feature Averaging Method for Kinship Verification

  • Niharika YadavEmail author
  • Aarti Goyal
  • T. MeenpalEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


Over a past few years, kinship verification using facial images has been gaining significant attention by different researchers. Kinship verification is motivated by the human inheritance and psychological findings that a child resembles his/her parent more than any other person in terms of facial appearance. In this paper, a new Feature Averaging method is proposed for kinship verification. This method creates an average vector that maximizes the variance between kinship pairs and minimizes the variance between non-kinship pairs. Multiple features are extracted using different feature descriptors for each facial image to create a high-dimensional feature vector. Principal Component Analysis (PCA) is used for dimension reduction of the high-dimensional feature vector by selecting apposite features which give useful information. The resultant PCA reduced feature vector characterizes each facial image with salient features. Experimental results are presented for different kinship datasets and the obtained verification accuracy demonstrates the performance of the proposed method.


Kinship verification Genetic similarity Feature averaging Feature descriptor 



This research work is supported for the research grant by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India. The sanctioned project title is “Design and development of an Automatic Kinship Verification system for Indian faces with possible integration of AADHAR Database.” with reference no. ECR/2016/001659.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Electronics and TelecommunicationNIT RaipurRaipurIndia

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