<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-30T04:34:21Z</responseDate><request verb="GetRecord" identifier="oai:munzur-rdlopen.rawdatalibrary.net:munzur/670" metadataPrefix="oai_dc">https://munzur-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:munzur-rdlopen.rawdatalibrary.net:munzur/670</identifier><datestamp>2026-10-28T15:28:03Z</datestamp><setSpec>openaire</setSpec><setSpec>driver</setSpec><setSpec>com_munzur_1</setSpec><setSpec>col_munzur_2</setSpec><setSpec>com_munzur_11</setSpec><setSpec>col_munzur_162</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>Facial emotion recognition on a dataset using Convolutional Neural Network</dc:title><dc:creator>Tümen, Vedat</dc:creator><dc:creator>Söylemez, Ömer Faruk</dc:creator><dc:creator>Ergen, Burhan</dc:creator><dc:subject>Convolution neural networks</dc:subject><dc:subject>Deep learning</dc:subject><dc:subject>Facial expression recognition</dc:subject><dc:subject>Image classification</dc:subject><dc:description>Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.</dc:description><dc:publisher>Institute of Electrical and Electronics Engineers Inc.</dc:publisher><dc:date>2017</dc:date><dc:type>Conference Object</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/munzur/670</dc:identifier><dc:identifier>https://doi.org/10.1109/idap.2017.8090281</dc:identifier><dc:identifier>978-153861880-6</dc:identifier><dc:language>tr</dc:language><dc:relation>IDAP 2017 - International Artificial Intelligence and Data Processing Symposium</dc:relation><dc:rights>info:eu-repo/semantics/openAccess</dc:rights><dc:type>info:eu-repo/semantics/conferenceObject</dc:type><dc:identifier>https://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/670</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>