I have used OpenCV's face detection and recognition capabilities for a couple of projects
- home security system using Odroid and IR camera modules, a side project for cat recognition,
testing low-res cheap USB cameras in low lighting - and have become fairly familiar with its gotchas.

Its weak area currently is the accuracy of face detection. Before recognizing the identity of a face,
you have to first find the positions of faces in an image.

OpenCV provides multiple algorithms for this - cascades of weak classifiers
likes Haar cascades, Local Binary Pattern cascades, Histogram of Gradients cascades -
and a number of pretrained models of frontal and profile human faces for each of those algorithms.
There's even a frontal cat face model!
But all of them suffer from high false positives or false negatives depending on
subject distance and ambient lighting levels.
The cat model has trouble with even the slightest of angles.

So face alignment is a mandatory pre-processing step with OpenCV's models.
But OpenCV doesn't provide any end-to-end alignment routines - it's all upto you to
write the alignment code.
The dlib library has all that built in.

Coming to OpenCV face recognition, it provides 3 approaches - eigen faces, fischer faces and LBPH faces.
Its docs explain the shortcomings of each well.
In theory, LBPH should give the best accuracy, but I consistently found Fischer giving the highest among the three.
Recognition too requires considerable preprocessing - left and right histogram equilization, cropping out hair and neck areas, etc.
All the pre-processing makes dataset preparation cumbersome.
But it does work okish - 65 to 75 percent accuracy - with smallish datasets of just 20 frontal faces per person.

If you plan to start with OpenCV for face capabilities, I suggest using dlib instead.

I haven't used OpenBR but eyeballing the code tells me it too uses OpenCV face APIs underneath
and another library named stasm which has face alignment capabilities similar to dlib but using OpenCV.
OpenBR seems to make building preprocessing pipelines easier using its own DSL -
that should reduce the trial and error time significantly.
But it doesn't add any new algorithm.

I haven't used OpenFace but looked into it in the past. It uses dlib for face detection and alignment,
and then uses deep convolutional neural network for feature extraction and recognition instead of eigen, fischer or LBPH.
These convolutional features are likely to do a better job than OpenCV's cascade features.
I'm not sure about the ideal training dataset size though.

Generally, in such cases where a dataset is likely to be small due to practical restrictions,
the preferred deep learning approach is transfer learning where a large pretrained model like ImageNet is used for initial layers
and only the last few layers are retrained on the user's face dataset.

I've used deep object detection frameworks like YOLO and ResNet R-CNN in other contexts,
and found them to be good for person detection. I think a deep object detection trained on faces to output face positions combined with
deep face recognition is the best combination.
FaceNet does exactly that (https://github.com/davidsandberg/facenet) and is probably the best one right now.

All said, identity recognition in our brains is actually multimodal (face, body, gait, voice, gesture, etc).
AFAIK, all the existing stacks support only frontal face recognition with some tolerance for transformations, and none of them support even recognition using profile face images, let alone multimodal identity recognition.