An Approach for Multi-pose Face Detection Exploring Invariance by Training

摘要

In this paper, a rotation invariant approach for face detection is proposed. The approach consists of training specific Haar cascades for ranges of in-plane face orientations, varying from coarse to fine. As the Haar features are not robust enough to cope with high in-plane rotations over many different images, they are trained only until an accented decay in precision is evident. When that happens, the range of orientations is divided up into sub-ranges, and this procedure continues until a predefined rotation range is reached. The effectiveness of the approach is evaluated on a face detection problem considering two well-known data sets: CMU-MIT [1] and FDDB [2]. When tested using CMU-MIT dataset, the proposed approach achieved accuracies higher than the traditional methods such as the ones proposed by Viola and Jones [3] and Rowley et al.[1]. The proposed approach has also achieved a large area under the ROC curve and true positive rates that were higher than the rates of all the published methods tested over the FDDB dataset.