English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper an approach based on modelling of recogni- tion error with Articial Neural Networks is presented to increase face emotion recognition in absence of any pre-processing or enhancement technique for feature extraction. This approach consists of two stages: in the rst stage an ANN structure is dened for the recognition task by means of a Genetic Algorithm (Recognition ANN - ReANN). Then this structure is used to perform recognition on a test set Y to estimate classication error probabilities. In the second stage an additional ANN is dened to associate these error patterns with correct classication pat- terns using the same test set (Corrective ANN - CoANN). The composite ANN system then is tested with a dierent set Z. In recognition tasks performed with the ReANN it was observed that some emotions were more likely to be incorrectly classied than others. This was further corroborated with perceptual data. With the integrated ANN system (ReANN plus CoANN) it was observed that some of these emotions could be recognised more accurately. In general overall recognition was increased from 75% to 85% with this approach.

Error Modelling Approach based on Artificial Neural Networks for Face Emotion Recognition