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Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation
Author(s) -
Chakraborti Tapabrata,
Chatterjee Amitava,
Halder Anisha,
Konar Amit
Publication year - 2015
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12104
Subject(s) - computer science , discriminative model , artificial intelligence , benchmark (surveying) , differential evolution , pattern recognition (psychology) , feature (linguistics) , discrete cosine transform , binary number , artificial neural network , algorithm , mathematics , image (mathematics) , linguistics , philosophy , arithmetic , geodesy , geography
The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm (GSA). The initial feature set is generated from the facial images by using the 2‐D discrete cosine transform (DCT) and then the proposed modified binary quantum GSA with differential mutation (MBQGSA‐DM) is utilized to select a sub‐set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network (ANN). Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion (JAFFE) database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.