TY - JOUR
T1 - Pose-invariant face recognition with multitask cascade networks
AU - Elharrouss, Omar
AU - Almaadeed, Noor
AU - Al-Maadeed, Somaya
AU - Khelifi, Fouad
N1 - Funding information: This publication was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - In this work, a face recognition method is proposed for face under pose variations using a multi-task convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module are combined in a cascaded structure and used separately. In the presence of various facial poses as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the Left side, Frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g. background content), we propose a skin-based face segmentation method using structure-decomposition and the Color Invariant Descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness.
AB - In this work, a face recognition method is proposed for face under pose variations using a multi-task convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module are combined in a cascaded structure and used separately. In the presence of various facial poses as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the Left side, Frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g. background content), we propose a skin-based face segmentation method using structure-decomposition and the Color Invariant Descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness.
KW - Face recognition
KW - Pose estimation
KW - Pose-invariant
KW - skin segmentation
KW - Convolutional neural networks
UR - http://www.scopus.com/inward/record.url?scp=85122679149&partnerID=8YFLogxK
U2 - 10.1007/s00521-021-06690-4
DO - 10.1007/s00521-021-06690-4
M3 - Article
SN - 0941-0643
VL - 34
SP - 6039
EP - 6052
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 8
ER -