A survey: Verification of family relationship from parents and child facial images

Verification of Family Relationship from facial images is a challenging problem in computer vision, and there are very few attempts on tackling this problem in the literature. We present a survey on how to verify family relation by a various metric learning method, probabilistic framework and several methods which can extract critical points on a face using both location and texture information such as lip corners, eye corners and nose tip. These are critical points in a human face, and centre of the mouth of each face. Motivated by the fact that interclass samples (without kinship relations) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the interclass samples (with kinship relations) are pushed as close as possible and interclass samples lying in a neighborhood are repulsed and pulled as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to further improve the verification performance it is necessary to develop a method to extract the salient familial traits in face images for kinship recognition. If this idea works, an instrument may be invented to measure familial relationships. This computational kinship measurement might have a large impact in real applications, such as child adoptions, trafficking/smuggling of children, and finding missing children, identifying relatives from a photo collection. We can collect Dataset of young parent and old parent face images from Internet. In our research work verification has to be performed by measuring the number of image pairs available for training and testing along with images of their parents and children frontal images. These images are taken as kinship so as to maintain relationship between two persons who are biologically related with overlapping genes.