Hi,
I've successfully installed and tested this tool on my Windows 10 machine, and I'm writing a simple procedure to install it. It may be useful for some people to try to use this tool on a Windows machine.

IMPORTANT: Actually, this project has been done for Linux systems, especially dlib. In my tests, the performance of this tool in Windows 10 was about a quarter compared to Ubuntu, built with the same specs. But I haven't seen any difference between these two in other subjects.

Read First:
The new version of dlib doesn't need Boost anymore, so you can skip it. Remember that you still need to meet the following requirements.
Requirments:
(I've used this tutorial with these tools installed on Windows 10, but the newer versions may work too.)

1. Microsoft Visual Studio 2015 (or newer) with C/C++ Compiler installed. (Visual C++ 2015 Build Tools didn't work for me, and I got into problems in compiling dlib)
2. Of course Python3 (I used Python3.5 x64 but the other versions may work too)
3. CMake for windows and add it to your system environment variables.
4. (ONLY FOR older versions of dlib) Boost library version 1.63 or newer. Also, you can use precompiled binaries for specific MSVC you have, but I don't suggest. (I've included the compiling procedure of Boost in this tutorial)

Installation:
Easy installation:
Just install dlib and face_recognition (not always on the newest version):
pip install dlib and then pip install face_recognition.

Manual installation:

1. Download and install scipy and numpy+mkl (must be mkl version) packages from this link (all credits goes to Christoph Gohlke). Remember to grab the correct version based on your current Python version.
2. Download Boost library source code or binary release for your current MSVC from this link.
3. If you downloaded the binary version skip to step 4 else, follow these steps to compile and build Boost by yourself:
   3-1. Extract the Boost source files into C:\local\boost_1_XX_X (X means the current version of Boost you have)
   3-2. Create a system variable with these parameters:
   Name: VS140COMNTOOLS
   Value: C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\Tools\ (or any path where you have installed MSVC)
   3-3. Open Developer Command Prompt for Visual Studio and go to the current directory of Boost extracted and try these commands to compile Boost:
   bootstrap
b2 -a --with-python address-model=64 toolset=msvc runtime-link=static
   3-4. If you successfully compile Boost, it should create compiled files in stage directory.
4. (If you have already compiled Boost skip this step) If you already download the binary release, extract the contents to C:\local\boost_1_XX_X
5. Grab the latest version of dlib from this repo and extract it.
6. Go to dlib directory and open cmd and follow these commands to build dlib: (remember to replace XX with the current version of Boost you have)
   set BOOST_ROOT=C:\local\boost_X_XX_X
set BOOST_LIBRARYDIR=C:\local\boost_X_XX_X\stage\lib
python setup.py install --yes USE_AVX_INSTRUCTIONS or python setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA
7. Now, you can use import dlib without any problem in your python script.
8. You can also check the current version of dlib with pip show dlib.
9. Now, install face_recognition with pip install face_recognition.
10. Enjoy!

Finally, I need to say thanks to @ageitgey and @davisking for their awesome work.