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Proceedings Paper

Convolutional neural networks with balanced batches for facial expressions recognition
Author(s): Elena Battini Sönmez; Angelo Cangelosi
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Paper Abstract

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.

Paper Details

Date Published: 17 March 2017
PDF: 6 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410J (17 March 2017); doi: 10.1117/12.2268412
Show Author Affiliations
Elena Battini Sönmez, Istanbul Bilgi Üniv. (Turkey)
Angelo Cangelosi, Plymouth Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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