Bio-inspired hybrid framework for multi-view face detection

McCarroll, N, Belatreche, A, Harkin, J and Li, Y 2015, 'Bio-inspired hybrid framework for multi-view face detection' , in: Neural Information Processing : 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV , Lecture Notes in Computer Science, 9492 (9492) , Springer International Publishing, pp. 232-239.

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Abstract

Reliable face detection in completely uncontrolled settings still remains a challenging task. This paper introduces a novel hybrid learning strategy that achieves robust in-plane and out-of-plane multi-view face detection through the enhanced implementation of the hierarchical bio-inspired HMAX framework using spiking neurons. Through multiple training trials, separate pools of neurons are trained on different face poses to extract features through feed-forward unsupervised STDP. The trained neurons are then processed by an additional STDP mechanism to generate a streamlined repository of broadly tuned multi-view neurons. After unsupervised feature extraction, supervised feature selection is implemented within the hybrid framework to reduce false positives. The hybrid system achieves robust invariant detection of in-plane and out-of-plane rotated faces that compares favourably with state-of-the-art face detection systems.

Item Type:	Book Section
Schools:	Schools > School of Computing, Science and Engineering
Publisher:	Springer International Publishing
Series Name:	Lecture Notes in Computer Science
ISBN:	9783319265605
ISSN:	0302-9743
Related URLs:	Publication
Funders:	Non funded research
Depositing User:	Yuhua Li
Date Deposited:	06 Jan 2016 09:33
Last Modified:	08 Aug 2017 17:34
URI:	http://usir.salford.ac.uk/id/eprint/37708

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