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access icon free Grey Wolf optimisation-based feature selection and classification for facial emotion recognition

© The Institution of Engineering and Technology
Received 13/09/2017, Accepted 06/02/2018, Revised 06/01/2018, Published 22/02/2018

The channels used to convey the human emotions consider actions, behaviours, poses, facial expressions, and speech. An immense research has been carried out to analyse the relationship between the facial emotions and these channels. The goal of this study is to develop a system for Facial Emotion Recognition (FER) that can analyse the elemental facial expressions of human, such as normal, smile, sad, surprise, anger, fear, and disgust. The recognition process of the proposed FER system is categorised into four processes, namely pre-processing, feature extraction, feature selection, and classification. After preprocessing, scale invariant feature transform -based feature extraction method is used to extract the features from the facial point. Further, a meta-heuristic algorithm called Grey Wolf optimisation (GWO) is used to select the optimal features. Subsequently, GWO-based neural network (NN) is used to classify the emotions from the selected features. Moreover, an effective performance analysis of the proposed as well as the conventional methods such as convolutional neural network, NN-Levenberg–Marquardt, NN-Gradient Descent, NN-Evolutionary Algorithm, NN-firefly, and NN-Particle Swarm Optimisation is provided by evaluating few performance measures and thereby, the effectiveness of the proposed strategy over the conventional methods is validated.

Inspec keywords: neural nets; particle swarm optimisation; emotion recognition; evolutionary computation; optimisation; face recognition; feature extraction; gradient methods

Other keywords: optimal features; feature extraction method; FER system; scale invariant feature; recognition process; pre-processing; facial point; NN-particle swarm optimisation; Grey Wolf optimisation; feature selection; facial emotions; facial emotion recognition; elemental facial expressions; human emotions; fear

Subjects: Optimisation techniques; Image recognition; Other topics in statistics; Computer vision and image processing techniques; Optimisation techniques; Neural computing techniques

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