From Face Recognition to Kinship Verification: An Adaptation Approach

Kinship verification in the wild is a challenging yet interesting issue, which aims to determine whether two unconstrained facial images are from the same family or not. Most previous methods for kinship verification can be divided as low-level hand-crafted features based shallow methods and kin data only trained convolutional neural network (CNN) based deep methods. Worthy of affirmation, numerous work in vision get that convolutional featu res are discriminative, but bigger data dependent. A fact is that for a variety of data-limited vision problems, such as limited Kinship datasets, the ability of CNNs is seriously dropped because ofoverfitting. To this end, by inheriting the success of deep mining algorithm s on fa ce verification (e.g. LFW), in this paper, we propose a Coarse-to-Fine Transfer (CFT) based deep kinship verification framework. As the idea implied, this paper tries to answer "is it possible to transfer a fa ce recognition net to kinship verification?". Therefore, a supervised coarse pre-training and domain-specific ad hoc fine re-training paradigm is exploited, with which the kinrelation specificfeatures are effectively capturedfromfaces. Extensive experiments on benchmark datasets demonstrate that our proposed CFT adaptation approach is comparable to the state-of-the art methods with a large margin.