Abstract
The long-history Chinese anthroposcopy has demonstrated the often satisfying capabilities to tell the characteristics (mostly exaggerated as fortune) of a person by reading his/her face, i.e. understanding the fine-grained facial attributes (e.g. single/double-fold eyelid, position of mole). In this paper, we study the face-reading problem from the computer vision perspective and present a computational face reader to automatically infer the characteristics of a person based on his/her face. For example, it can estimate the attractive and easy-going characteristics of a Chinese person from his/her big eyes according to the Chinese anthroposcopy literature. Specifically, to well estimate these fine-grained facial attributes, we propose a novel deep convolutional network in which a facial region pooling layer (FRP layer) is embedded, called FRP-net. The FRP layer uses the searched facial region windows (locates these facial attributes) instead of the commonly-used sliding windows. The experiments on facial attribute estimation demonstrate the potential of the automatic face reader framework, and qualitative and quantitative evaluations from the attractive and smart perspectives of face reading validate the excellence of the presented face reader framework.
This work was performed when X. Shu was visiting National University of Singapore.
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It is estimated by our gender recognition system [7].
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Here, we also use the random Gaussian values as the parameter initialization. However, it produces less stable results and slower convergence.
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Acknowledgments
This work was partially supported by the 973 Program of China (Project No. 2014CB347600), the National Natural Science Foundation of China (Grant No. 61522203 and 61402228), and the Program for New Century Excellent Talents in University under Grant NCET-12-0632.
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Shu, X., Zhang, L., Tang, J., Xie, GS., Yan, S. (2016). Computational Face Reader. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_10
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DOI: https://doi.org/10.1007/978-3-319-27671-7_10
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