Title: Bee royalty offspring algorithm for improvement of facial expressions classification model

Authors: Amir Jamshidnezhad; Md Jan Nordin

Addresses: Department of Computer Science, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran ' Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

Abstract: A major issue which divides the facial expressions from the other classification domains is complicated behaviour of human to express the emotions which should be recognised with the classifier model. Existing research recognise the emotions using a range of classification techniques. However, low accuracy rate, large training set, large extracted features or priority for sequence images are the main drawbacks of those works. One of the recent techniques to address the facial expressions problem is fuzzy rule-based system (FRBS) which is used as a successful method to model and solve the natural-based problems. However, FRBS is poor to adapt the existing knowledge with the diverse conditions. In this article a novel hybrid genetic-fuzzy rule-based model is proposed to optimise the performance of fuzzy classification while the limited raw input data as the features are used. In this model, the proposed genetic algorithm simulates the honey bees offspring generation process called bee royalty offspring algorithm (BROA) to improve the training process of classic genetic algorithm. The comparison results illustrated that the genetic-fuzzy classification model improves considerably the accuracy rate and performance of FRBS while the BROA modify the training process of genetic-based algorithms.

Keywords: genetic algorithms; fuzzy classification; fuzzy rule-based systems; FRBS; bee royalty offspring algorithm; BROA; facial expressions recognition; facial recognition; fuzzy logic; emotions; honey bees.

DOI: 10.1504/IJBIC.2013.055092

International Journal of Bio-Inspired Computation, 2013 Vol.5 No.3, pp.175 - 191

Received: 16 Mar 2013
Accepted: 16 Mar 2013
Published online: 31 Mar 2014 *

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