Audio-visual emotion recognition using FCBF feature selection method and particle swarm optimization for fuzzy ARTMAP neural networks

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• 作者：Davood Gharavian ; Mehdi Bejani ; Mansour Sheikhan
• 关键词：Audio ; visual emotion recognition ; Particle swarm optimization ; Fuzzy ARTMAP neural network
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：2
• 页码：2331-2352
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

Humans use many modalities such as face, speech and body gesture to express their feeling. So, to make emotional computers and make the human-computer interaction (HCI) more naturally and friendly, computers should be able to understand human feelings using speech and visual information. In this paper, we recognize the emotions from audio and visual information using fuzzy ARTMAP neural network (FAMNN). Audio and visual systems fuse at decision and feature levels. Finally, the particle swarm optimization (PSO) is employed to determine the optimum values of the choice parameter (α), the vigilance parameters (ρ), and the learning rate (β) of the FAMNN. Experimental results showed that the feature-level and decision-level fusions improve the outcome of unimodal systems. Also PSO improved the recognition rate. By using the PSO-optimized FAMNN at feature level fusion, the recognition rate was improved by about 57 % with respect to the audio system and by about 4.5 % with respect to the visual system. The final emotion recognition rate on the SAVEE database was reached to 98.25 % using audio and visual features by using optimized FAMNN.