TY - GEN
T1 - LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild
AU - Elharrouss, Omar
AU - Almaadeed, Noor
AU - Al-Maadeed, Somaya
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose estimation model is trained using a self-collected dataset built from three popular datasets including FLW, CEP, and CASIA-WebFace using three categories of face image capture such as Left side, Frontal and right side. Experimental evaluation has been conducted using two datasets: Pointing'04 and Schneiderman. Results reveal the robustness of the proposed pose estimation model. Moreover, the proposed face pose estimation is applied on three datasets to widen the dataset and make it bigger for training and testing deep learning models.
AB - In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose estimation model is trained using a self-collected dataset built from three popular datasets including FLW, CEP, and CASIA-WebFace using three categories of face image capture such as Left side, Frontal and right side. Experimental evaluation has been conducted using two datasets: Pointing'04 and Schneiderman. Results reveal the robustness of the proposed pose estimation model. Moreover, the proposed face pose estimation is applied on three datasets to widen the dataset and make it bigger for training and testing deep learning models.
KW - CNN
KW - Face recognition
KW - pose estimation
KW - pose-invariant face dataset
UR - https://www.scopus.com/pages/publications/85085495207
UR - https://www.scopus.com/pages/publications/85085495207#tab=citedBy
U2 - 10.1109/ICIoT48696.2020.9089530
DO - 10.1109/ICIoT48696.2020.9089530
M3 - Conference contribution
AN - SCOPUS:85085495207
T3 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
SP - 124
EP - 130
BT - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Y2 - 2 February 2020 through 5 February 2020
ER -