TY - GEN
T1 - Real-Time CNN Based Facial Emotion Recognition Model for a Mobile Serious Game
AU - Anto-Chavez, Carolain
AU - Maguiña-Bernuy, Richard
AU - Ugarte, Willy
N1 - Publisher Copyright:
© 2024 by SCITEPRESS – Science and Technology Publications, Lda.
PY - 2024
Y1 - 2024
N2 - Every year, the increase in human-computer interaction is noticeable. This brings with it the evolution of computer vision to improve this interaction to make it more efficient and effective. This paper presents a CNN-based emotion face recognition model capable to be executed on mobile devices, in real time and with high accuracy. Different models implemented in other research are usually of large sizes, and although they obtained high accuracy, they fail to make predictions in an optimal time, which prevents a fluid interaction with the computer. To improve these, we have implemented a lightweight CNN model trained with the FER-2013 dataset to obtain the prediction of seven basic emotions. Experimentation shows that our model achieves an accuracy of 66.52% in validation, can be stored in a 13.23MB file and achieves an average processing time of 14.39ms and 16.06ms, on a tablet and a phone, respectively.
AB - Every year, the increase in human-computer interaction is noticeable. This brings with it the evolution of computer vision to improve this interaction to make it more efficient and effective. This paper presents a CNN-based emotion face recognition model capable to be executed on mobile devices, in real time and with high accuracy. Different models implemented in other research are usually of large sizes, and although they obtained high accuracy, they fail to make predictions in an optimal time, which prevents a fluid interaction with the computer. To improve these, we have implemented a lightweight CNN model trained with the FER-2013 dataset to obtain the prediction of seven basic emotions. Experimentation shows that our model achieves an accuracy of 66.52% in validation, can be stored in a 13.23MB file and achieves an average processing time of 14.39ms and 16.06ms, on a tablet and a phone, respectively.
KW - Emotion
KW - Expression
KW - FER
KW - Facial
KW - Machine Learning
KW - Mobile
KW - Real-Time
KW - Recognition
UR - https://www.scopus.com/pages/publications/85193959213
U2 - 10.5220/0012683800003699
DO - 10.5220/0012683800003699
M3 - Contribución a la conferencia
AN - SCOPUS:85193959213
T3 - International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
SP - 84
EP - 92
BT - Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2024
A2 - Mulvenna, Maurice
A2 - Perez, Maria Lozano
A2 - Ziefl e, Martina
PB - Science and Technology Publications, Lda
T2 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2024
Y2 - 28 April 2024 through 30 April 2024
ER -