Locally Rejected Metric Learning Based False Positives Filtering for Face Detection

Face detection in the wild needs to deal with various challenging conditions, which often leads to the situation where intraclass difference of faces exceeds interclass difference between faces and nonfaces. Based on this observation, in this paper we propose a locally rejected metric learning (LRML) based false positives filtering method. We firstly learn some prototype faces with affinity propagation clustering algorithm, and then apply locally rejected metric learning to seek a linear transformation to reduce the differences between each face and prototype faces while enlarging the differences between non-faces and prototype faces and preserving the distribution of learned prototype faces with locally rejected term. With the learned transformation, data are mapped into a new domain where face can be exactly detected. Results on FDDB and a self-collected dataset indicate our method is better than Viola-Jones face detectors. And the combination of the two methods shows an improvement in face detection.