Bio-Inspired Hybrid Framework for Multi-view Face Detection

N McCarroll, Ammar Belatreche, Jim Harkin, Y Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
Publication statusPublished (in print/issue) - 1 Nov 2015
Event22nd International Conference on Neural Information Processing (ICONIP2015) -
Duration: 1 Nov 2015 → …


Conference22nd International Conference on Neural Information Processing (ICONIP2015)
Period1/11/15 → …


  • Spiking neural networks
  • face detection
  • pose estimation
  • multi-view


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