LinArc -使用LinCos和ArcFace的深度人脸识别

Ravi Chopra, J. Dhar, Vinal Patel
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引用次数: 2

摘要

数据每天都在泛滥。由于数据和可用计算能力的激增,人脸识别的使用正在迅速加快步伐。这一领域的研究正以难以想象的速度进行,准确率已经达到了99%以上,这可能是由于贝叶斯的误差造成的。然而,仍有试验的空间。本文尝试将ArcFace和LinCos这两种新颖的人脸识别思想混合在一起构建模型。在本文中,目标是通过结合LinCos的思想来操纵ArcFace使用的加性角边际损失。我们使用带有修正损失函数的Mobile FaceNet重新训练预训练的ArcFace模型。结果表明,与ArcFace和LinCos模型相比,我们的模型以更快的速度优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LinArc - Deep Face Recognition Using LinCos And ArcFace
Data is overflowing day by day. The use of face recognition is rapidly picking up pace due to the boom in data and available computation power. The research done in this field is at an unimaginable pace, and accuracies of more than 99% have been achieved, which are possibly less only by Baye’s error. However, there is still room for experimentation. This paper tries to build a model by mixing two novel ideas of face recognition - ArcFace and LinCos. In this paper, the target is to manipulate the Additive Angular Margin Loss used by ArcFace by incorporating the ideas of LinCos. We re-train the pre-trained ArcFace model using Mobile FaceNet with a modified loss function. The results suggest that our model optimizes at a faster rate as compared to the ArcFace and LinCos models.
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