A Constrained Generative Model Applied to Face Detection

摘要

A generative neural network model, constrained by non-face examples chosen by an iterative algorithm, is applied to face detection. To improve the generalization ability of the model, another constraint based on the minimum description length is added. This model is tested and compared with another state-of-the-art face detection system on a large image test set collected at CMU.