An Analysis of the Viola-Jones Face Detection Algorithm
Yi-Qing Wang
→ BibTeX
    title   = {{An Analysis of the Viola-Jones Face Detection Algorithm}},
    author  = {Wang, Yi-Qing},
    journal = {{Image Processing On Line}},
    volume  = {4},
    pages   = {128--148},
    year    = {2014},
    doi     = {10.5201/ipol.2014.104},
% if your bibliography style doesn't support doi fields:
    note    = {\url{}}
Yi-Qing Wang, An Analysis of the Viola-Jones Face Detection Algorithm, Image Processing On Line, 4 (2014), pp. 128–148.

Communicated by Jose-Luis Lisani
Demo edited by Yi-Qing Wang


In this article, we decipher the Viola-Jones algorithm, the first ever real-time face detection system. There are three ingredients working in concert to enable a fast and accurate detection: the integral image for feature computation, Adaboost for feature selection and an attentional cascade for efficient computational resource allocation. Here we propose a complete algorithmic description, a learning code and a learned face detector that can be applied to any color image. Since the Viola-Jones algorithm typically gives multiple detections, a post-processing step is also proposed to reduce detection redundancy using a robustness argument.