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> wrote: > 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? >