Deep Learning for Biometric Recognition of Children using Footprints

V. Kamble, M. Dale
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引用次数: 1

Abstract

Children are the most important part of society. Every parent is concerned about their health and security. The children's age group of 0 to 5 years is extremely vulnerable. New security options need to be found for the children in this age group. Biometric recognition using their footprint will be an emerging trend for children. This research uses footprint crease pattern of children for recognition. The crease pattern on footprints is extracted for the features. The database of 48 children is collected from preschools and neighborhoods. These images are preprocessed and enhanced. The Transfer learning approach of deep learning is used to compare the proposed method of identification of children. Different deep learning algorithms VGG16, VGG19, ResNet50, AlexNet are used. The proposed method is a fine tuned, customized AlexNet model. The comparison of parameters used is done for all algorithms. Proposed model reduces the number of parameters by 1,69,30,688 with the accuracy of 98 %.
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基于脚印的深度学习儿童生物特征识别
儿童是社会最重要的组成部分。每个家长都关心自己的健康和安全。0至5岁的儿童是非常脆弱的。需要为这个年龄段的儿童找到新的安全选择。对儿童来说,利用他们的足迹进行生物识别将是一种新兴趋势。本研究利用儿童足迹折痕图进行识别。提取足迹上的折痕图作为特征。该数据库从幼儿园和社区收集了48名儿童。这些图像经过预处理和增强。使用深度学习的迁移学习方法对所提出的儿童识别方法进行了比较。使用了不同的深度学习算法VGG16, VGG19, ResNet50, AlexNet。所提出的方法是一个微调的、定制的AlexNet模型。对所有算法使用的参数进行了比较。该模型减少了1,69,30,688个参数,精度达到98%。
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