Show simple item record

AuthorElharrouss O.
AuthorAlmaadeed N.
AuthorAl-Maadeed, Somaya
AuthorKhelifi F.
Available date2022-05-19T10:23:06Z
Publication Date2022
Publication NameNeural Computing and Applications
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/s00521-021-06690-4
URIhttp://hdl.handle.net/10576/31081
AbstractIn this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions 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 texture 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) and 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.
SponsorThis 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.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectConvolution
Convolutional neural networks
Gesture recognition
Image segmentation
Textures
Cascade networks
Convolutional neural network
Face identification
Face images
Face segmentation
Neural network model
Pose invariant
Pose-estimation
Pose-invariant face recognition
Skin segmentation
Face recognition
TitlePose-invariant face recognition with multitask cascade networks
TypeArticle
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record