Abstract
Face tracking methods are increasingly critical for many expression mapping analysis applications, along its research track, deep convolutional neural network (DCNN-based) search techniques have attracted broad interests due to their high efficiency in 3D feature points. In this paper, we focus on the problem of 3D feature point’s extraction and expression mapping using a light-weight deep convolutional neural network (LW-DCNN) search and data conversion model, respectively. Specifically, we proposed novel light-weight deep convolutional neural network for 3D feature point’s extraction to solve the great initial shape errors in regression cascaded framework and the slow processing speed in traditional CNN. Furthermore, an effective data conversion model is proposed to generate the deformation coefficient to realize the expression mapping. Extensive experiments on several benchmark image databases validate the superiority of the proposed approaches.
Supported by the Fundamental Research Funds for the Central Universities of China (No. A03013023001050, No. ZYGX2016J095). Natural Science Foundation of Sichuan (No. 2017JY0229). CERNET Innovation Project (NGII20170805). National Natural Science Foundation of China (Grant Nos. 61502083 and 61872066).
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Song, J., Rao, Y., Ji, P., Pu, J., Chen, K. (2019). Light-Weight DCNN for Face Tracking. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_17
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DOI: https://doi.org/10.1007/978-981-13-9190-3_17
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