I guess one of the questions is what is your false negative rate in
Approach 1 Step 1?
Ofcourse if you are limited by resources you may have to go with Approach 1.
On Thu, Oct 6, 2016 at 6:14 AM, venito camelas <robotirlan...@gmail.com>
> I'm designing a prototype using *Hadoop* for video processing to do face
> recognition. I thought of 2 ways of doing it.
> *Approach 1:*
> I was thinking of doing something in 2 steps:
> 1. A map that receives frames and if a face is found it gets stored
> for the next step.
> 2. A map that receives the frames from step 1 (all frames containing 1
> face at least) and does face recognition.
> Step 1 would be ran only once while step 2 runs every time I want
> recognize a new face.
> *Approach 2:*
> The other approach I thought about is to do face recognition to all the
> data every time
> The first approach saves time because I don't have to process faceless
> frames every time I want to do face recognition, it also uses more disk
> space (and it could be a lot of space).
> I'm not sure whats better. Is it a bad thing to leave that precomputed
> frames there forever?