Determine attention of faces through growing level of emotion using deep Convolution Neural Network

Kumar, Rajneesh; Kumar, Gupta Anuj; Garain, Jogendra; Kisku, Dakshina Ranjan; Sanyal, Goutam

doi:10.1109/icicict1.2017.8342699

“…Several studies have shown that variations of the neural network algorithm produce better performance than other algorithms. Kumar et al (2017) proposed the deep convolution neural network for the determine the emotion level [1]. Based on a test of the CK + dataset, the convolution neural network performs better than the SVM that has problems in multiclass classification.…”

Section: Introductionmentioning

confidence: 99%

The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition

Kirana¹,

Wibawanto²,

Hidayah³

et al. 2019

EECSI

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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 a neural network for two concurrent processes consisting of face detection and emotional recognition. 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 the 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. It can be concluded that our method works accurately. The proposed method is superior to the state-of-theart algorithms

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“…Several studies have shown that variations of the neural network algorithm produce better performance than other algorithms. Kumar et al (2017) proposed the deep convolution neural network for the determine the emotion level [1]. Based on a test of the CK + dataset, the convolution neural network performs better than the SVM that has problems in multiclass classification.…”

Section: Introductionmentioning

confidence: 99%

The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition

Kirana¹,

Wibawanto²,

Hidayah³

et al. 2019

EECSI

0

View full text Add to dashboard Cite

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 a neural network for two concurrent processes consisting of face detection and emotional recognition. 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 the 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. It can be concluded that our method works accurately. The proposed method is superior to the state-of-theart algorithms

show abstract