Especially when detecting in images with many faces, these legacy off the shelf things built on Viola-Jones type models or HoG feature extractors are just not acceptable by comparison with deep learning models.
And even at that, you need to fine tune the model to your own specific dataset with appropriate weights to reflect asymmetry in false positives vs false negatives. Simply using any off the shelf model, even a deep CNN model, virtually never works in practice. Unless your real life task is well approximated by the academic data set used for training (and it never is), you’re going to need a computer vision engineer involved.
For face detection DLib and OpenCV work really work in real world applications. As I mentioned I've deployed two real-world solutions using them in the past 6 months.
However, I'm surprised it's that bad.
I've done two projects in the last 6 months that required face detection, and in both cases combinations of DLib and OpenCV performed perfectly well. Since these are entirely off-the-self models I don't see why Rekognition should - in principle - be any worse.