Kinship Verification with Deep Convolutional Neural Networks

Kaihao Zhang, Yongzhen Huang, Chunfeng Song, Hong Wu and Liang Wang

Abstract

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 key-points-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.

Session

Poster 2

Files

Extended Abstract (PDF, 637K)
Paper (PDF, 990K)

DOI

10.5244/C.29.148
https://dx.doi.org/10.5244/C.29.148

Citation

Bibtex

@inproceedings{BMVC2015_148,
	title={Kinship Verification with Deep Convolutional Neural Networks},
	author={Kaihao Zhang and Yongzhen Huang and Chunfeng Song and Hong Wu and Liang Wang},
	year={2015},
	month={September},
	pages={148.1-148.12},
	articleno={148},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.148},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.148}
}