Update README.md

README.md

@@ -88,7 +88,7 @@ More complex systems include features such as <b>Face Liveness Detection</b> (to
 # Problem:
 
 <p>
-libfaceid democratizes learning Face Recognition. Popular models such as FaceNet and OpenFace are not straightforward to use and don't provide easy-to-follow guidelines on how to install and setup. So far, dlib has been the best in terms of documentation and usage but it is slow on CPU and has too many abstractions (abstracts OpenCV as well). Simple models such as OpenCV is good but too basic and lacks documentation of the parameter settings, on classification algorithms and end-to-end pipeline. Pyimagesearch has been great having several tutorials with easy to understand explanations but not much emphasis on model comparisons and seems to aim to sell books so intentions to help the community are not so pure after all (I hate the fact that you need to wait for 2 marketing emails to arrive just to download the source code for the tutorials. But I love the fact that he replies to all questions in the threads). With all this said, I've learned a lot from all these resources so I'm sure you will learn a lot too. 
+libfaceid democratizes learning Face Recognition. Popular models such as FaceNet and OpenFace are not straightforward to use and don't provide easy-to-follow guidelines on how to install and setup. So far, dlib has been the best in terms of documentation and usage but installation is not straightforward, it is slow on CPU and is highly abstracted (abstracts OpenCV as well). Simple models such as OpenCV is good but too basic and lacks documentation of the parameter settings, on classification algorithms and end-to-end pipeline. Pyimagesearch has been great having several tutorials with easy to understand explanations but not much emphasis on model comparisons and seems to aim to sell books so intentions to help the community are not so pure after all (I hate the fact that you need to wait for 2 marketing emails to arrive just to download the source code for the tutorials. But I love the fact that he replies to all questions in the threads). With all this said, I've learned a lot from all these resources so I'm sure you will learn a lot too. 
 
 libfaceid was created to somehow address these problems and fill-in the gaps from these resources. It seamlessly integrates multiple models for each step of the pipeline enabling anybody specially beginners in Computer Vision and Deep Learning to easily learn and experiment with a comprehensive face recognition end-to-end pipeline models. No strings attached. Once you have experimented will all the models and have chosen specific models for your specific use-case and system requirements, you can explore the more advanced models like FaceNet.