Bengali Ethnicity Recognition and Gender Classification using CNN & Transfer Learning

M. Jewel, Md. Ismail Hossain, Tamanna Haider Tonni
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引用次数: 4

Abstract

In this paper, we have demonstrated how to apply CNN (Convolutional Neural Network) structured model and transfer learning to identify the ethnicity of Bengali people and it's a systematic process of gender classification too. We also applied several models of transfer learning like VGG16, Mobilenet, Resnet50, etc. to find out which model is more convenient to get our desired accuracy. But problems arise because there are many Indian people who look like and get dressed up like Bengali since in India many Bengali dwell in when many of them speak Bangla as well! (people of Kolkata along with some other provinces). So, the Bengali people are not only found in Bangladesh but also elsewhere in the world. That's why our model is based on facial images along with the tradition of their costumes. We tried to build a sophisticated model using CNN and transfer learning for this purpose and we got some tremendous performances applying transfer learning.
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使用CNN和迁移学习的孟加拉民族识别和性别分类
在本文中,我们展示了如何应用CNN(卷积神经网络)结构化模型和迁移学习来识别孟加拉人的种族,这也是一个系统的性别分类过程。我们还应用了VGG16, Mobilenet, Resnet50等几种迁移学习模型,看看哪种模型更方便得到我们想要的精度。但是问题出现了,因为有许多印度人看起来像孟加拉人,穿着像孟加拉人,因为在印度有许多孟加拉人居住,而他们中的许多人也说孟加拉语!(加尔各答和其他一些省份的人)。因此,孟加拉人不仅在孟加拉国,而且在世界其他地方都有。这就是为什么我们的模型是基于面部图像和他们的传统服装。为此,我们尝试使用CNN和迁移学习建立一个复杂的模型,应用迁移学习我们获得了一些惊人的表现。
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