I am working on building a custom facial recognition for our office.
I am planning to use Google FaceNet, Now my question is that you can find or create your own version of facenet model in keras or pytorch there's no issue in that, but regarding creating dataset ,I want to know what are the best practices to capture photo of person when I don't have any prior photo of that person,all I have is a camera and a person ,should I create variance in by changing lightning condition or orientation or face size ?
A properly trained FaceNet model should already be somewhat invariant to lighting conditions, pose and other features that should not be a part of identifying a face. At least that is what is claimed in a draft of the FaceNet paper. If you only intend to compare feature vectors generated from the network, and intend to recognize a small group of people, your own dataset likely does not have to be particulary large.
Personally I have done something quite similar to what you are trying to achieve for a group of around ~100 people. The dataset consisted of 1 image per person and I used a 1-N-N classifier to classify the generated feature vectors. While I do not remember the exact results, it did work quite well. The pretrained network's architecture was different from FaceNet's but the overall idea was the same though.
The only way to truly answer your question though would be to experiment and see how well things work out in practice.