Abstract
The most fundamental problem of local feature based kinship verification methods is that a local feature can capture the variations of environmental conditions and the differences between two persons having a kin relation, which can significantly decrease the performance. To address this problem, we propose a feature subtraction method to remove the kinship unrelated part from the local feature through a linear function of which only one parameter, namely a subtraction matrix, needs to be inferred from training data. This is done by using a gradient descent method to simultaneously minimize the feature distance between face image pairs with kinship and maximize the distance between non-kinship pairs. Based on the subtracted feature, the verification is realized through a simple Gaussian based distance comparison method. Experiments on two public databases show that the feature subtraction method outperforms or is comparable to state-of-the-art kinship verification methods. Copyright ©2015 by IEEE.
Original language | English |
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Title of host publication | IEEE International Conference on Image Processing (ICIP), 2015 |
Publisher | IEEE Signal Processing Society |
Publication date | 2015 |
Pages | 1573 - 1577 |
ISBN (Electronic) | 978-1-4799-8339-1 |
DOIs | |
Publication status | Published - 2015 |
Event | IEEE International Conference on Image Processing - Québec City, Canada Duration: 27 Sept 2015 → 30 Sept 2015 |
Conference
Conference | IEEE International Conference on Image Processing |
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Country/Territory | Canada |
City | Québec City |
Period | 27/09/2015 → 30/09/2015 |