期刊
出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001422520164
关键词
Bidirectional Elman neural network; classification; deep learning; enhanced battle royale optimization; facial emotion recognition; feature extraction; feature selection
This paper introduces a novel facial emotion recognition (FER) system that maximizes classification accuracy. The system consists of phases including pre-processing, feature extraction, feature selection, and classification. It utilizes extended cascaded filter for image pre-processing, extracts geometric and appearance-based features, employs enhanced battle royale optimization for feature selection, and uses bidirectional Elman neural network for emotion classification. The proposed system achieved high accuracy rates in evaluations.
Facial emotion recognition (FER) is an interesting area of research. It has a wide range of applications, but there is still a deficiency of an accurate approach to provide better results. A novel FER system to maximize classification accuracy has been introduced in this paper. The proposed approach constitutes the following phases: pre-processing, feature extraction, feature selection, and classification. Initially, the images are pre-processed using the extended cascaded filter (ECF) and then the geometric and appearance-based features are extracted. An enhanced battle royale optimization (EBRO) for feature selection has been proposed to select the relevant features and to reduce the dimensionality problem. Then, the classification is carried out using a novel bidirectional Elman neural network (Bi-ENN) that offers high classification results. The proposed Bi-ENN-based emotion classification can accurately discriminate the input features. It enabled the model to predict the labels for classification accurately. The proposed model on evaluations attained an accuracy rate of 98.57% on JAFFE and 98.75% on CK+ datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据