scholarly journals Humanoid Robot Assisted Training for Facial Expressions Recognition Based on Affective Feedback

Author(s):  
Eleuda Nunez ◽  
Soichiro Matsuda ◽  
Masakazu Hirokawa ◽  
Kenji Suzuki
Author(s):  
A. Loza-Alvarez ◽  
A.E. Monroy-Meza ◽  
R. A. Suarez-Rivera ◽  
G. I. Perez-Soto ◽  
L. A. Morales-Hernandez ◽  
...  

2021 ◽  
Author(s):  
Hongxiang Gao ◽  
Shan An ◽  
Jianqing Li ◽  
Chengyu Liu

2020 ◽  
Vol 9 (3) ◽  
pp. 1208-1219
Author(s):  
Hendra Kusuma ◽  
Muhammad Attamimi ◽  
Hasby Fahrudin

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Andry Chowanda

AbstractSocial 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%.


Author(s):  
Sandra Costa ◽  
Filomena Soares ◽  
Cristina Santos

2020 ◽  
Vol 234 ◽  
pp. 111440
Author(s):  
Noor Amalina Ramli ◽  
Anis Nurashikin Nordin ◽  
Norsinnira Zainul Azlan

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