Bio-inspired hierarchical framework for multi-view face detection and pose estimation

McCarroll, N, Belatreche, A, Harkin, J and Li, Y 2015, 'Bio-inspired hierarchical framework for multi-view face detection and pose estimation' , in: Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers (IEEE), Article number 7280674.

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Abstract

Face detection is one of the most active research areas in computer vision. Despite the well documented success of classical machine learning techniques in controlled situations, face detection in completely uncontrolled settings remains a difficult task. Recent progress with bio-inspired approaches have addressed challenging areas of invariance including scale, occlusion and illumination issues but there remains a lack of concentrated effort into truly multi-view detection of faces in different poses and orientations. This paper introduces a novel strategy to address this through the enhanced implementation of a hierarchical bio-inspired HMAX framework using spiking neurons that implements feature extraction with unsupervised STDP. A multiple trial training scheme is introduced to train separate pools of neurons on different face poses. The trained neurons are then processed by an additional STDP mechanism to generate a streamlined repository of broadly tuned multi-view neurons. Experimental results demonstrate that the new system achieves robust invariant detection of in-plane and out-of-plane rotated faces with single face per image datasets. In addition, extending the multi-view system by introducing lateral inhibition between merged pools of multi-view face detecting neurons, results in a single model that is able to achieve simultaneous face detection and accurate face pose estimation.

Item Type:	Book Section
Additional Information:	International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12 July 2015 through 17 July 2015
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	International Joint Conference on Neural Networks (IJCNN) 2015
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
ISBN:	978147991964
Related URLs:	Organisation
Funders:	Non funded research
Depositing User:	Yuhua Li
Date Deposited:	27 Jul 2015 10:57
Last Modified:	15 Feb 2022 17:36
URI:	https://usir.salford.ac.uk/id/eprint/35773

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