Abstract
In this paper, we present a discriminative feature difference learning method for facial image based kinship verification. To transform feature difference of an image pair to be discriminative for kinship verification, a linear transformation matrix for feature difference between an image pair is inferred from training data. This transformation matrix is obtained through minimizing the difference of L2 norm between the feature difference of each kinship pair and its neighbors from non-kinship pairs. To find the neighbors, a cosine similarity is applied. Our method works on feature difference rather than the commonly used feature concatenation, leading to a low complexity. Furthermore, there is no positive semi-definitive constrain on the transformation matrix while there is in metric learning methods, leading to an easy solution for the transformation matrix. Experimental results on two public databases show that the proposed method combined with a SVM classification method outperforms or is comparable to state-of-the-art kinship verification methods.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work is supported by the Danish Council for Independent Research | Technology and Production Sciences under grant number: 1335-00162 (iSocioBot).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Anal. Mach. Intell. 36, 331–345 (2014)
Fang, R., Tang, K., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 1577–1580 (2010)
Zhou, X., Lu, J., Hu, J., Shang, Y.: Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments. In: Proceedings of the 20th ACM International Conference on Multimedia. MM 2012, New York, NY, USA, pp. 725–728. ACM (2012)
Zhou, X., Hu, J., Lu, J., Shang, Y., Guan, Y.: Kinship verification from facial images under uncontrolled conditions. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 953–956. ACM (2011)
Yan, H., Lu, J., Zhou, X.: Prototype-based discriminative feature learning for kinship verification. IEEE Trans. Cybern. 9(1), 51–61 (2014)
Duan, X., Tan, Z.H.: A feature subtraction method for image based kinship verification under uncontrolled environments. In: IEEE International Conference on Image Processing (ICIP) (2015)
Kulis, B.: Metric learning: A survey. Found. Trends Mach. Learn. 5, 287–364 (2012)
Xing, E.P., Jordan, M.I., Russell, S., Ng, A.Y.: Distance metric learning with application to clustering with side-information. In: Advances in Neural Information Processing Systems, pp. 505–512 (2002)
Shao, M., Xia, S., Fu, Y.: Genealogical face recognition based on ub kinface database. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 60–65 (2011)
Yan, H., Lu, J., Deng, W., Zhou, X.: Discriminative multimetric learning for kinship verification. IEEE Trans. Inform. Forensics Secur. 9, 1169–1178 (2014)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 85 (2008)
Lu, J., Hu, J., Zhou, X., Shang, Y., Tan, Y.P., Wang, G.: Neighborhood repulsed metric learning for kinship verification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2594–2601. IEEE (2012)
Lu, J., Hu, J.: Kinship face in the wild. (http://www.kinfacew.com/) Accessed August 20, 2015
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Zhu, C., Byrd, R.H., Lu, P., Nocedal, J.: Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw. (TOMS) 23, 550–560 (1997)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/cjlin/libsvm
Kan, M., Shan, S., Xu, D., Chen, X.: Side-information based linear discriminant analysis for face recognition. In: BMVC, pp. 1–12 (2011)
Lu, J., Hu, J., Liong, V., Zhou, X., Bottino, A., Ul Islam, I., Figueiredo Vieira, T., Qin, X., Tan, X., Chen, S., Mahpod, S., Keller, Y., Zheng, L., Idrissi, K., Garcia, C., Duffner, S., Baskurt, A., Castrillon-Santana, M., Lorenzo-Navarro, J.: The fg 2015 kinship verification in the wild evaluation. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–7 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Duan, X., Tan, ZH. (2015). Neighbors Based Discriminative Feature Difference Learning for Kinship Verification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-27863-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27862-9
Online ISBN: 978-3-319-27863-6
eBook Packages: Computer ScienceComputer Science (R0)