LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild
| Author | Elharrouss O. |
| Author | Almaadeed N. |
| Author | Al-Maadeed, Somaya |
| Available date | 2022-05-19T10:23:10Z |
| Publication Date | 2020 |
| Publication Name | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1109/ICIoT48696.2020.9089530 |
| Abstract | 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. |
| Sponsor | identity. Compared with the recent and related techniques, the proposed system has been shown to outperform related works and deliver state-of-the-art performance for pose estimation. The built large-scale dataset for training may improve the accuracy of the proposed system as increasing the number of images for each identity will cover a wider range of variations of the face. Also, the generated dataset can be improved in terms of the number of identities using the same methodology on other datasets like VGGFace2. Acknowledgment 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. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Convolutional neural networks Deep learning Internet of things Statistical tests Experimental evaluation Face pose estimation Facial Expressions Low illuminations Pose estimation Pose-invariant face recognition Three categories Training and testing Face recognition |
| Type | Conference Paper |
| Pagination | 124-130 |
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