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

Abstract

AbstractIn 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.Keywordsface detectionorientation invariance by trainingadaboosthaar featurestree of classifiers