Abstract
Deep feature is widely applied in many fields such as image retrieval, image classification, face verification, etc. All the post-processing methods using deep feature make some assumptions about feature distribution. However, in most situations, features do not follow the hypothesised distribution approximately. In this paper, we focus on face-verification applications which also suffer from these problems. We propose an up-sample method called IUSM to alleviate the problems caused by biased samples. Additionally, by analyzing the Joint Bayesian model theoretically and practically, we propose a feature fusion method called LFF which utilizes the distribution properties of Joint Bayesian. Based on IUSM, the face verification accuracy of biased data is improved by 6 % while generalization ability of convolution network is not crippled. On the widely used Labeled Face in the Wild(LFW) dataset, LFF method can slightly improve the accuracy 0.15 % while the baseline accuracy is more than 97.51 %. We also argue that LFF can improve each deep face verification algorithm which uses Joint Bayesian model due to LFF’s linear combination of features.
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Qu, Z., Li, X., Dou, Y., Yang, K. (2016). Face Verification Algorithm with Exploiting Feature Distribution. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_33
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DOI: https://doi.org/10.1007/978-3-319-42911-3_33
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