Model compression of SDM‐based face alignment for mobile applications
Model compression of SDM‐based face alignment for mobile applications
复制标题
DOI：
10.1049/joe.2018.8294
复制
发表时间：
2018-08
影响因子：
--
通讯作者：
Yehu Shen;Quansheng Jiang;Yang Yong;Bangfu Wang;Qixin Zhu
中科院分区：
文献类型：
--
作者：
Yehu Shen;Quansheng Jiang;Yang Yong;Bangfu Wang;Qixin Zhu
人脸对齐可以广泛应用于人脸识别、表情识别、基于人脸的AR应用等领域。近年来，基于级联回归的人脸对齐算法以其较低的计算成本和在非受控场景下令人印象深刻的结果而受到欢迎。不幸的是，对于基于级联回归的方法，训练模型的大小相当大，这使得它不适合在手机上的商业应用。在本研究中，作者提出了一种基于监督下降法（SDM）训练模型的数据压缩方法。首先，采用非参数方法估计模型数据的分布；然后提出了一种自适应量化算法对模型数据进行量化。最后，将自适应量化算法与SDM训练过程紧密结合，对结果进行微调。定量实验结果证明，该方法可以在不影响性能的情况下将数据模型压缩到原始大小的20%以下。将提出的方法集成到一个移动AR应用中，主观评价证明，与未压缩的方法相比，提出的压缩方法可以提供相似的视觉效果。
Face alignment could be widely used in face recognition, expression recognition, face-based AR applications etc. Cascaded-regression-based face alignment algorithms have been popular in recent years for their low computational costs and impressive results in uncontrolled scenarios. Unfortunately, the size of the trained model is quite large for cascaded-regression-based methods which makes it unsuitable for commercial applications on mobile phones. In this study, the authors proposed a data compression method for the trained model of the supervised descent method (SDM). Firstly, the distribution of the model data was estimated using a non-parametric method. Then an adaptive quantisation algorithm was proposed to quantise the model data. Finally, their adaptive quantisation algorithm was tightly coupled with the SDM training process to fine tune the results. The quantitative experimental results proved that their proposed method could compress the data model to <20% of its original size without hurting the performances. The proposed method has been integrated into a mobile AR application, subjective evaluations proved that the proposed compression method could provide similar visual effects compared with the uncompressed counterpart.