Update readme.md

readme.md

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 - Training is only supported on Linux. To train new model from scratch, more requirements are needed on top of the requirements listed in the testing stage.
 - [Facenet](https://github.com/davidsandberg/facenet) provided by 
-Sandberg et al.. In our paper, we use a face recognition network trained with in-house face data which cannot be made publicly available due to the company policies. To make our work reproducible, we recommend using this alternative face recognition model. We use the version [20170512-110547](https://github.com/davidsandberg/facenet/blob/529c3b0b5fc8da4e0f48d2818906120f2e5687e6/README.md) trained on MS-Celeb-1M. Training process has been tested with the new model to ensure a comparable result.
+Sandberg et al. In our paper, we use a network to exrtact perceptual face features. This network model cannot be made publicly released. As an alternative, we recommend using the Facenet by Sandberg et al. This repo uses the version [20170512-110547](https://github.com/davidsandberg/facenet/blob/529c3b0b5fc8da4e0f48d2818906120f2e5687e6/README.md) trained on MS-Celeb-1M. Training process has been tested with this  model to ensure similar results.
 - [Resnet50-v1](https://github.com/tensorflow/models/blob/master/research/slim/README.md) pre-trained on ImageNet from Tensorflow Slim. We use the version resnet_v1_50_2016_08_28.tar.gz as an initialization of the face reconstruction network.
 - [68-facial-landmark detector](https://drive.google.com/file/d/1KYFeTb963jg0F47sTiwqDdhBIvRlUkPa/view?usp=sharing). We use 68 facial landmarks for loss calculation during training. To make the training process reproducible, we provide a lightweight detector that produce comparable results to [the method of Bulat et al.](https://github.com/1adrianb/2D-and-3D-face-alignment). The detector is trained on [300WLP](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm), [LFW](http://vis-www.cs.umass.edu/lfw/), and [LS3D-W](https://www.adrianbulat.com/face-alignment).