Towards an Spiking Deep Belief Network for Face Recognition Application

Mazdak Fatahi (1, 2) , Mahmood Ahmadi (2) , Arash Ahmadi (2) , Mahyar Shahsavari (1) , Philippe Devienne (1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 Razi University of Kermanshah

Understanding brain mechanisms and its problem solving techniques is the motivation of many emerging brain inspired computation methods. In this paper, respecting deep architecture of the brain and spiking model of biological neural networks, we propose a spiking deep belief network to evaluate ability of the deep spiking neural networks in face recognition application on ORL dataset. To overcome the change of using spiking neural networks in a deep learning algorithm, Siegert model is utilized as an abstract neuron model. Although there are state of the art classic machine learning algorithms for face detection, this work is mainly focused on demonstrating capabilities of brain inspired models in this era, which can be serious candidate for future hardware oriented deep learning implementations. Accordingly, the proposed model, because of using leaky integrate-and-fire neuron model, is compatible to be used in efficient neuromorphic platforms for accelerators and hardware implementation.

Domaines: Informatique et langage [cs.CL]; Intelligence artificielle [cs.AI]

Mazdak Fatahi, Mahmood Ahmadi, Arash Ahmadi, Mahyar Shahsavari, Philippe Devienne. Towards an Spiking Deep Belief Network for Face Recognition Application. 6th International Conference on Computer and Knowledge Engineering (ICCKE 2016), Oct 2016, Mashhad Iran. ⟨hal-01382624⟩