Sonmez, Elena BattiniCangelosi, Angelo2024-07-182024-07-182017978-1-5106-1132-00277-786X1996-756Xhttps://doi.org/10.1117/12.2268412https://hdl.handle.net/11411/78699th International Conference on Machine Vision (ICMV) -- NOV 18-20, 2016 -- Nice, FRANCEThis 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.eninfo:eu-repo/semantics/closedAccessAffective ComputingConvolutional Neural NetworksFacial Expression RecognitionConference Object2-s2.0-8502995582210.1117/12.2268412N/A10341N/AWOS:000410664800018