TY - JOUR
T1 - Real time facial expression recognition system based on deep learning
AU - Bustamante, Jose Carlos
AU - Rodriguez, Ciro
AU - Esenarro, Doris
PY - 2019/9/1
Y1 - 2019/9/1
N2 - ©BEIESP. The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..
AB - ©BEIESP. The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..
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U2 - 10.35940/ijrte.B1591.0982S1119
DO - 10.35940/ijrte.B1591.0982S1119
M3 - Article
SN - 2277-3878
SP - 4047
EP - 4051
JO - International Journal of Recent Technology and Engineering
JF - International Journal of Recent Technology and Engineering
ER -