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?
