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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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|
|Funders:||Non funded research|
|Depositing User:||Yuhua Li|
|Date Deposited:||06 Jan 2016 09:33|
|Last Modified:||06 Jan 2016 09:33|
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