High-performance and Lightweight Real-time Deep Face Emotion Recognition

Justus Schwan*, Esam Ghaleb, Enrique Hortal, Stylianos Asteriadis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

Deep learning is used for all kinds of tasks which require human-like performance, such as voice and image recognition in smartphones, smart home technology, and self-driving cars. While great advances have been made in the field, results are often not satisfactory when compared to human performance. In the field of facial emotion recognition, especially in the wild, Convolutional Neural Networks (CNN) are employed because of their excellent generalization properties. However, while CNNs can learn a representation for certain object classes, an amount of (annotated) training data roughly proportional to the class's complexity is needed and seldom available. This work describes an advanced pre-processing algorithm for facial images and a transfer learning mechanism, two potential candidates for relaxing this requirement. Using these algorithms, a lightweight face emotion recognition application for Human-Computer Interaction with TurtleBot units was developed.
Original languageEnglish
Title of host publication12th International Workshop on Semantic and Social Media (SMAP), Bratislava
PublisherIEEE
Pages76-79
Number of pages4
ISBN (Print)9781538607565
DOIs
Publication statusPublished - 2017
Event12th International Workshop on Semantic and Social Media Adaptation and Personalization - Bratislava, Slovakia
Duration: 9 Jul 201710 Jul 2017
Conference number: 12
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=58804

Workshop

Workshop12th International Workshop on Semantic and Social Media Adaptation and Personalization
Abbreviated titleSMAP
Country/TerritorySlovakia
CityBratislava
Period9/07/1710/07/17
Internet address

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