Abstract
Face recognition is one of the challenging domains. We have seen artificial neural network perform very well in both detection and recognition. In this paper, we propose a novel method of feature extraction where features obtained at the end of hidden layer of neural network is utilised. This hidden layer output is our first level of features. On these features, we apply binary particle swarm optimisation (BPSO) to remove the redundancy, the few hidden units in the network. BPSO over hidden layer outputs can be implemented in two ways: 1) to apply BPSO over hidden layer in the training stage so the network is better optimised; 2) to directly use the BPSO on an optimised neural network's hidden layer output. Both the techniques performed well over traditional neural network and conventional BPSO. Experiments on FERET and LFW datasets show promising results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Intelligence Paradigms
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
