TY - GEN
T1 - Emotion, Age and Gender Prediction Through Masked Face Inpainting
AU - Islam, Md Baharul
AU - Hosen, Md Imran
N1 - Publisher Copyright:
© 2023, Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Prediction of gesture and demographic information from the face is complex and challenging, particularly for the masked face. This paper proposes a deep learning-based integrated approach to predict emotion and demographic information for unmasked and masked faces, consisting of four sub-tasks: masked face detection, masked face inpainting, emotion, age, and gender prediction. The masked face detector module provides a binary decision on whether the face mask is available or not by applying pre-trained MobileNetV3. We use the inpainting module based on U-Net embedding with ImageNet weights to remove the face mask and restore the face. We use the convolutional neural networks to predict emotion (e.g., happy, angry). Besides, VGGFace-based transfer learning has been used to predict demographic information (e.g., age, gender). Extensive experiments on five publicly available datasets: AffectNet, UTKFace, FER-2013, CelebA, and MAFA, show the effectiveness of our proposed method to predict emotion and demographic identification through masked face reconstruction.
AB - Prediction of gesture and demographic information from the face is complex and challenging, particularly for the masked face. This paper proposes a deep learning-based integrated approach to predict emotion and demographic information for unmasked and masked faces, consisting of four sub-tasks: masked face detection, masked face inpainting, emotion, age, and gender prediction. The masked face detector module provides a binary decision on whether the face mask is available or not by applying pre-trained MobileNetV3. We use the inpainting module based on U-Net embedding with ImageNet weights to remove the face mask and restore the face. We use the convolutional neural networks to predict emotion (e.g., happy, angry). Besides, VGGFace-based transfer learning has been used to predict demographic information (e.g., age, gender). Extensive experiments on five publicly available datasets: AffectNet, UTKFace, FER-2013, CelebA, and MAFA, show the effectiveness of our proposed method to predict emotion and demographic identification through masked face reconstruction.
KW - Emotion Prediction
KW - Face Inpainting
KW - Face detection
UR - http://www.scopus.com/inward/record.url?scp=85171577831&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-37660-3_3
DO - 10.1007/978-3-031-37660-3_3
M3 - Conference contribution
AN - SCOPUS:85171577831
SN - 9783031376597
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 48
BT - Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges - Proceedings
A2 - Rousseau, Jean-Jacques
A2 - Kapralos, Bill
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Pattern Recognition, ICPR 2022
Y2 - 21 August 2022 through 25 August 2022
ER -