  In this paper, we propose a method to apply the popular cascade classifier
into face recognition to improve the computational efficiency while keeping
high recognition rate. In large scale face recognition systems, because the
probability of feature templates coming from different subjects is very high,
most of the matching pairs will be rejected by the early stages of the cascade.
Therefore, the cascade can improve the matching speed significantly. On the
other hand, using the nested structure of the cascade, we could drop some
stages at the end of feature to reduce the memory and bandwidth usage in some
resources intensive system while not sacrificing the performance too much. The
cascade is learned by two steps. Firstly, some kind of prepared features are
grouped into several nested stages. And then, the threshold of each stage is
learned to achieve user defined verification rate (VR). In the paper, we take a
landmark based Gabor+LDA face recognition system as baseline to illustrate the
process and advantages of the proposed method. However, the use of this method
is very generic and not limited in face recognition, which can be easily
generalized to other biometrics as a post-processing module. Experiments on the
FERET database show the good performance of our baseline and an experiment on a
self-collected large scale database illustrates that the cascade can improve
the matching speed significantly.
