Bio-Inspired Hybrid Framework for Multi-view Face Detection

  • Niall McCarrollEmail author
  • Ammar Belatreche
  • Jim Harkin
  • Yuhua Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9492)


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.


Multi-view face detection Spiking neural networks STDP Hybrid learning Hierarchical object detection HMAX 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Niall McCarroll
    • 1
    Email author
  • Ammar Belatreche
    • 1
  • Jim Harkin
    • 1
  • Yuhua Li
    • 2
  1. 1.Intelligent Systems Research CentreUniversity of UlsterDerryNorthern Ireland
  2. 2.School of Computing, Science and EngineeringUniversity of SalfordManchesterUK

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