lbrandy.com » Blog Archive » How We Made Our Face Recognizer 25x Faster - The Diigo Meta page

• 29 Oct 08
  

  harry palmer

  programming algorithms optimization python parellelism multicore

  • How We Made Our Face Recognizer 25x Faster

    
    
    

    
    

    This is a war story from my real day job. I work at a start-up company in Pittsburgh that does face detection, tracking, and recognition software. This particular problem involved optimization of the face recognizer.

    
    

    I’m going to try to simplify our face recognition algorithm as much as possible to get to the point I’m trying to make. If you are reading this to try to learn our algorithmic secrets, you’ll be sorely disappointed. What you’ll see here is in the first few chapters of a pattern recognition textbook.

    
    

    Recently, we came out with a brand new version of our face recognizer. As is usually the case, once they are happy with the accuracy they hand it off to me to see if I can speed it up.

• 28 Oct 08
  

  Joel Liu

  algorithm face recognition

• 

  j p
  • tree based vector quantization
  • only look up a child node if you mean it
  • 4 more annotations...
  • • current node has all of the information necessary to choose which child to follow
    • improved the performance of the single threaded recognizer by a factor of 6
    • loop reordering requires a set of input faces to work properly
    • It would be natural for a programmer to decide that you need a compare(face1, face2) function. Unfortunately, however, implementing multiple comparisons by making multiple calls to a single compare() function leads to the misuse of cache.
• 27 Oct 08
  

  abditus

  Programing

• 

  Talat Fakhri

  Algorithms Furl Quantips

Would you like to comment?

Join Diigo for a free account, or sign in if you are already a member.