Bio-Inspired Hybrid Framework for Multi-view Face Detection
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.
KeywordsMulti-view face detection Spiking neural networks STDP Hybrid learning Hierarchical object detection HMAX
- 1.Zhang, C., Zhang, Z.: A survey of recent advances in face detection. MSR-TR-2010-66. Microsoft Research (2010)Google Scholar
- 10.Masquelier, T., Thorpe, S.: Learning to recognize objects using waves of spikes and spike timing-dependent plasticity. In: International joint conference on neural networks (IJCNN), Barcelona (2010)Google Scholar
- 11.McCarroll, N., Belatreche, A., Harkin, J., Li, Y.: Bio-inspired hierarchical framework for multi-view face detection and pose estimation. accepted for publication In: International joint conference on neural networks (IJCNN), Killarney (2015)Google Scholar
- 12.Google Picasa 3.9. http://picasa.google.com/