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The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset
Arabian Journal for Science and Engineering ( IF 2.9 ) Pub Date : 2021-05-11 , DOI: 10.1007/s13369-021-05693-6
Muhammed Ali Erbir , Halil Murat Ünver

In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems.



中文翻译:

如何提高VGGFace2数据集上人脸识别的准确性

在这项研究中,研究了面部识别技术的发展。提出了一些方法,通过使用带有VGGFace2数据集和4个不同CNN模型的转移学习来提高面部识别的准确率。尽管其中一些经过测试的报价降低了准确率,但其中一些却有所提高。确定直方图平衡,扩展训练数据,提取图像的非面部部分和垂直对齐图像对准确率的影响,并将其与原始图像的准确率进行比较。作为最佳解决方案,首选从InceptionV3模型进行转移学习,进行垂直定位,并且在100个人的VGGFace2子集中使用10%的图像进行测试和90%的训练时,可以达到95.47%的准确率。数据集。在LFW中

更新日期:2021-05-11
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