Multi-modal Asian Conversation Mobile Video Dataset for Recognition Task

Suryani, Dewi; Ekaputra, Valentino; Chowanda, Andry

doi:10.11591/ijece.v8i5.pp4042-4046

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The SEMAINE Dataset has 24 interaction sessions with a total of 95 character interactions and 190 video clips [24]. Some datasets in the facial expressions recognition area also were collected with Asian respondents, for example, The Japanese Female Facial Expression (JAFFE) [25], Multimodal Asian Conversation Dataset [26], and the Indonesian Mixed emotion Dataset (IMED) [27]. The Japanese Female Facial Expression (JAFFE) [25] provides 7 classification of emotions (six basic emotions and neutral) from 213 images of 10 subjects.…”

Section: Datasetsmentioning

confidence: 99%

“…The Japanese Female Facial Expression (JAFFE) [25] provides 7 classification of emotions (six basic emotions and neutral) from 213 images of 10 subjects. The Multimodal Asian Conversation Dataset [26] provides seven classifications of emotions (six basic emotions and neutral) from more than 100 minutes of videos of 5 subjects. Finally, the Indonesian Mixed emotion Dataset (IMED) [27] consists of 570 videos and 66,819 Images categorised into seven single emotions (Anger, Disgust, Fear, Happy, Sadness, Surprise and Neutral) and twelve mixed emotions [27].…”

Section: Datasetsmentioning

confidence: 99%

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Separable convolutional neural networks for facial expressions recognition

Chowanda

2021

J Big Data

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Social interactions are important for us, humans, as social creatures. Emotions play an important part in social interactions. They usually express meanings along with the spoken utterances to the interlocutors. Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor. Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition. However, most of the current models of convolutional neural networks require an enormous computational power to train and process emotional recognition. This research aims to build compact networks with depthwise separable layers while also maintaining performance. Three datasets and three other similar architectures were used to be compared with the proposed architecture. The results show that the proposed architecture performed the best among the other architectures. It achieved up to 13% better accuracy and 6–71% smaller and more compact than the other architectures. The best testing accuracy achieved by the architecture was 99.4%.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Separable convolutional neural networks for facial expressions recognition

Chowanda

2021

J Big Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SEMAINE Dataset has 24 interaction sessions with a total of 95 character interactions and 190 video clips [19]. Some dataset in the facial expressions recognition area also were collected with Asian respondents, for example, JAFFE [20], Multimodal Asian Conversation Dataset [21], and the Indonesian Mixed emotion Dataset (IMED) [22]. The Japanese Female Facial Expression (JAFFE) [20] provides 7 classification of emotions (six basic emotions and neutral) from 213 images of 10 subjects.…”

Section: Datasetsmentioning

confidence: 99%

“…The Japanese Female Facial Expression (JAFFE) [20] provides 7 classification of emotions (six basic emotions and neutral) from 213 images of 10 subjects. The Multimodal Asian Conversation Dataset [21] provides seven classifications of emotions (six basic emotions and neutral) from more than 100 minutes of videos of 5 subjects. Finally, the Indonesian Mixed emotion Dataset (IMED) [22] consists of 570 videos and 66,819 Images categorised into seven single emotions (Anger, Disgust, Fear, Happy, Sadness, Surprise and Neutral) and twelve mixed emotions [22].…”

Section: Datasetsmentioning

confidence: 99%

Separable Convolutional Neural Networks For Facial Expressions Recognition

Chowanda

2021

Preprint

Self Cite

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Social interactions are important for us, human, as social creatures. Emotions play an important part in social interactions. They usually express meanings along with the spoken utterances to the interlocutors. Automatic facial expressions recognition is one technique to automatically capture, recognise, and understand emotions from the interlocutor. Many techniques proposed to increase the accuracy of emotions recognition from facial cues. Architecture such as convolutional neural networks demonstrates promising results for emotions recognition. However, most of the current models of convolutional neural networks require an enormous computational power to train and process emotional recognition. This research aims to build compact networks with depthwise separable layers while also maintaining performance. Three datasets and three other similar architectures were used to be compared to the proposed architecture. The results show that the proposed architecture performed the best among the other architectures. It achieved up to 13% better accuracy and 6-71% smaller and more compact than the other architectures. The best testing accuracy achieved by the architecture was 99.4%.

show abstract