  We propose a very simple, efficient yet surprisingly effective feature
extraction method for face recognition (about 20 lines of Matlab code), which
is mainly inspired by spatial pyramid pooling in generic image classification.
We show that features formed by simply pooling local patches over a multi-level
pyramid, coupled with a linear classifier, can significantly outperform most
recent face recognition methods. The simplicity of our feature extraction
procedure is demonstrated by the fact that no learning is involved (except PCA
whitening). We show that, multi-level spatial pooling and dense extraction of
multi-scale patches play critical roles in face image classification. The
extracted facial features can capture strong structural information of
individual faces with no label information being used. We also find that,
pre-processing on local image patches such as contrast normalization can have
an important impact on the classification accuracy. In particular, on the
challenging face recognition datasets of FERET and LFW-a, our method improves
previous best results by more than 10% and 20%, respectively.
