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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Gigih Prasetyo Cahyono
Software Engineering Visionet Data International
Computer Science & IT Electrical & Electronics Engineering

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The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition Kartika Candra Kirana; Slamet Wibawanto; Nur Hidayah; Gigih Prasetyo Cahyono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1938

Abstract

In this study, we present the improved artificial neural network based on cosine similarity in facial emotion recognition. We apply a shifting window that employs neural network for two concurrent processes consisting of face detection and emotional recognition. In order to prevent the slow and futile computations, non-face areas need to be filtered from neurons on each network layer, thus we propose the improved artificial neural network based on cosine similarity. Cosine similarity is employed to bypass the process of non-face areas in neural network. The accuracy of the proposed method reaches 0.84, while the accuracy of the original neural network method reaches 0.74. It can be concluded that our methods work accurately.proposed method is superior to the state-of-the-art algorithms.
Co-Authors Kartika Candra Kirana Nur Hidayah Slamet Wibawanto