A Novel Sample-Enhanced Dataset based on MFF for Large-Angle Face Recognition

Wang, He and Wang, Yan and Liu, Jie and Ying, Guisheng (2020) A Novel Sample-Enhanced Dataset based on MFF for Large-Angle Face Recognition. In: Mobimedia 2020, 27-28 August 2020, Cyberspace.

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Abstract

Large-angle face recognition has always been a huge challenge due to the scarcity of large-angle dataset. In this paper, a novel sample-enhanced dataset is constructed, which is composed of various angle face picture samples from -90° to 90° relative to the front face. The constructed dataset is obtained by enhancing large-angle face samples of the CASIA-WebFace dataset. The large-angle face samples are generated from small-angle face samples of the CASIA-WebFace dataset, which is based on the multi-task feature framework (MFF). By employing these sample datasets, four trained FaceNets are achieved for face recognition. Finally, to test the effectiveness of the four face recognition networks for the large-angle face, 300 large-angle face pictures of different basketball players are selected as the samples of the experiment. The results demonstrate that the accuracy of large-angle face recognition has been greatly improved when utilizing the FaceNet that is trained by the novel enhanced dataset.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: enhanced-dataset large angle mff face recognition
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor I.
Date Deposited: 04 Feb 2021 14:21
Last Modified: 04 Feb 2021 14:21
URI: https://eprints.eudl.eu/id/eprint/869

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