Convolutional neural networks with balanced batches for facial expressions recognition
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Spie-Int Soc Optical Engineering
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
9th International Conference on Machine Vision (ICMV) -- NOV 18-20, 2016 -- Nice, FRANCE
Anahtar Kelimeler
Affective Computing, Convolutional Neural Networks, Facial Expression Recognition
Kaynak
Ninth International Conference on Machine Vision (Icmv 2016)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
10341
