TY - GEN
T1 - Analysis of hand-crafted and learned feature extraction methods for real-Time facial expression recognition
AU - Olivares-Mercado, Jesus
AU - Toscano-Medina, Karina
AU - Sanchez-Perez, Gabriel
AU - Portillo-Portillo, Jose
AU - Perez-Meana, Hector
AU - Benitez-Garcia, Gibran
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - This paper presents an analysis of hand-crafted and learned feature extraction methods for real-Time facial expression recognition (FER). Our analysis focuses on methods capable of running on mobile devices, including traditional algorithms such as Gabor transform, HOG, LBP, as well as two compact CNN models, named Mobilenet V1 and V2. Additionally, we test the performance of MOTIF, a highly efficient texture feature extractor algorithm. Furthermore, we analyze the contribution of the mouth and front-eyes regions for recognizing the seven basic facial expressions. Experimental results are evaluated on two publicly available datasets. KDEF database which was captured under controlled conditions and RAF database which represents more naturalistic expressions captured in-The-wild. Under the same experimental conditions, MOTIF presents the fastest performance by sacrificing accuracy, while Mobilenet V2 presents the highest results with considerable speed and model size.
AB - This paper presents an analysis of hand-crafted and learned feature extraction methods for real-Time facial expression recognition (FER). Our analysis focuses on methods capable of running on mobile devices, including traditional algorithms such as Gabor transform, HOG, LBP, as well as two compact CNN models, named Mobilenet V1 and V2. Additionally, we test the performance of MOTIF, a highly efficient texture feature extractor algorithm. Furthermore, we analyze the contribution of the mouth and front-eyes regions for recognizing the seven basic facial expressions. Experimental results are evaluated on two publicly available datasets. KDEF database which was captured under controlled conditions and RAF database which represents more naturalistic expressions captured in-The-wild. Under the same experimental conditions, MOTIF presents the fastest performance by sacrificing accuracy, while Mobilenet V2 presents the highest results with considerable speed and model size.
KW - MOTIF
KW - Mobile applications
KW - Mobilenets
KW - ROI
KW - facial expression recognition
UR - http://www.scopus.com/inward/record.url?scp=85068475856&partnerID=8YFLogxK
U2 - 10.1109/IWBF.2019.8739178
DO - 10.1109/IWBF.2019.8739178
M3 - Contribución a la conferencia
AN - SCOPUS:85068475856
T3 - 2019 7th International Workshop on Biometrics and Forensics, IWBF 2019
BT - 2019 7th International Workshop on Biometrics and Forensics, IWBF 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Workshop on Biometrics and Forensics, IWBF 2019
Y2 - 2 May 2019 through 3 May 2019
ER -