A hierarchical learning network for face detection with in-plane rotation

Citation data:

Neurocomputing, Vol: 71, Issue: 16, Page: 3253-3263

Publication Year:
2008
Usage 22
Abstract Views 17
Downloads 5
Repository URL:
https://ro.uow.edu.au/era/1625
Author(s):
Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam
Tags:
era2012
article description
This paper presents a scale and rotation invariant face detection system. The system employs a hierarchical neural network, called SICoNNet, whose processing elements are governed by the nonlinear mechanism of shunting inhibition. The neural network is used as a face/nonface classifier that can handle in-plane rotated patterns. To train the network as a rotation invariant face classifier, an enhanced bootstrap training technique is developed, which prevents bias towards the nonface class. Furthermore, a multiresolution processing is employed for scale invariance: an image pyramid is formed through subsampling and face detection is performed at each scale of the pyramid using an adaptive threshold. Evaluated on the benchmark CMU rotated face database, the proposed face detection system outperforms some of the existing rotation invariant face detectors; it has fewer false positives and higher detection accuracy.