From Face Recognition to Kinship Verification: An Adaptation Approach

Qingyan Duan, Lei Zhang, Wangmeng Zuo; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1590-1598

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 features 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 of overfitting. To this end, by inheriting the success of deep mining algorithms on face 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 face 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 kin-relation specific features are effectively captured from faces. 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.