Convolutional neural networks with balanced batches for facial expressions recognition

E.B. Sonmez, A. Cangelosi

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


This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the
classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing
elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.
Original languageEnglish
Title of host publicationSPIE Proceedings
Number of pages6
Publication statusPublished - 2017
EventNinth International Conference on Machine Vision (ICMV 2016) - Nice, France
Duration: 18 Nov 201620 Nov 2016


ConferenceNinth International Conference on Machine Vision (ICMV 2016)
Abbreviated titleICMV


  • Affective computing
  • Convolutional Neural Networks
  • facial expression recognition


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