AdvKin: Adversarial Convolutional Network for Kinship Verification
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Lei Zhang, Qingyan Duan, and Wei Jia |
| Journal/Conference Name | Chinese Conference on Biometric Recognition |
| Paper Category | ECE |
| Paper Abstract | Kinship verification in the wild is an interesting and challenging problem, which aims to determine whether two unconstrained facial images are from the same family. Most previous methods for kinship verification can be divided as low-level hand-crafted features based shallow methods and kin data trained generic convolutional neural network (CNN) based deep methods. Nevertheless, these general methods cannot well mining the potential information implied in kin-relation data. Inspired by MMD and GAN, Adv-Kin method is proposed in this paper. The discrimination of deep features can be improved by introducing MMD loss (ML) to minimize the distribution difference between parents domain and children domain. In addition, we propose the adversarial loss (AL) that can further improve the robustness of CNN model. Extensive experiments on the benchmark KinFaceW-I, KinFaceW-II, Cornell KinFace and UB KinFace show promising results over many state-of-the-art methods. |
| Date of publication | 2017 |
| Code Programming Language | C++ |
| Comment |
Copyright Researcher 2022