Kinship verification from facial images is an interesting and challenging problem. The current algorithms on this topic typically represent faces with multiple low-level features, followed by a shallow learning model. However, these general manual features cannot well discover information implied in facial images for kinship verification, and thus even current best algorithms are not satisfying. In this paper, we propose to extract high-level features for kinship verification based on deep convolutional neural networks. Our method is end-to-end, without complex pre-processing often used in traditional methods. The high-level features are produced from the neuron activations of the last hidden layer, and then fed into a soft-max classifier to verify the kinship of two persons. Considering the importance of facial key-points, we also extract keypoints-based features for kinship verification. Experimental results demonstrate that our proposed approach is very effective even with limited training samples, largely outperforming the state-of-the-art methods. On two most widely used kinship databases, our method achieves 5.2% and 10.1% improvements compared with the previous best one, respectively.
For instance, Zhang et al. REF proposed a deep convolutional neural network (CNN) architecture for kinship verification from face images.
Kinship Verification with Deep Convolutional Neural Networks
BMVC
Computer Science