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.
This work is supported by the Danish Council for Independent Research | Technology and Production Sciences under grant number: 1335-00162 (iSocioBot).
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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
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