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Labeling faces with names based on the name semantic network

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Abstract

In this study, we propose a method of labeling faces with names in a large number of news images with captions. Other works explored facial similarities to label faces with names that are sensitive to the intra-person appearance variations, and the captions can offer the cues for the correlations of candidate names. Our method combines textual similarity from image captions with visual similarity of face collections of candidate name to automatically recognize celebrities. It does not require any supervisory inputs. It includes two main steps. Firstly, we build a name semantic network based on textual and visual similarity. Secondly, we apply a name semantic network to label face images with names. We perform experiments on the data set which consists of approximate half a million news images from Yahoo news. The experimental results show that the performance of our method is better than the existing algorithms.

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Acknowledgments

This work is supported by the doctorate foundation of Northwestern Polytechnical University under CX201114, Ministry of Education Fund for Doctoral Students Newcomer Awards of China, National Natural Science Foundation of China under Grant 61075014, 61272285, 61103062, The Research Fund for the Doctoral Program of Higher Education under Grant 20106102110028, 20116102110027, 20116102120031, 20126101110022, The Science and Technology project of Shaanxi Province under Grant 2013 K06-29, and NPU Basic Research Foundation under Grant JC201249.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China

    Xueping Su, Jinye Peng, Xiaoyi Feng & Jun Wu

  2. Computer Science College, Xi’an Polytechnic University, Xi’an, China

    Xueping Su

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  1. Xueping Su
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  2. Jinye Peng
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  3. Xiaoyi Feng
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  4. Jun Wu
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Correspondence to Xueping Su.

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Su, X., Peng, J., Feng, X. et al. Labeling faces with names based on the name semantic network. Multimed Tools Appl 75, 6445–6462 (2016). https://doi.org/10.1007/s11042-015-2581-x

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  • DOI: https://doi.org/10.1007/s11042-015-2581-x

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