Open AccessJournal Article10.22214/ijraset.2023.53470

Missing Child Identification using Convolutional Neural Network

- 30 Jun 2023

- International Journal For Science Techno...

- Vol. 11, Iss: 6, pp 380-384

TL;DR: In this paper , the authors used facial recognition to precisely identify a missing child from a dataset of missing child photographs and trained a deep learning model to do so, which is a potent approach for image-based applications.

Abstract: The goal of this study is to locate missing children from a big dataset of child photographs using a novel application of deep learning algorithms. The method makes use of facial recognition to precisely identify a missing child from a dataset of missing child photographs and trains a deep learning model to do so. Convolutional Neural Network (CNN), a potent approach for image-based applications, is the method used.

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Most frequently asked questions

1. What are the main reasons for children disappearing in India?

Children in India disappear due to various reasons such as kidnapping, abduction, running away, human trafficking, and getting lost. These factors contribute to the alarming number of missing children, with half of them never being located. The current circumstances put these children at risk of exploitation and abuse. According to the NCRB report, over one lakh children were reported missing in a year, and by the end of the year, 55,625 of them were still unaccounted for. This issue was highlighted in Parliament by the Ministry of Home Affairs on 20-03-2018.

2. How does the proposed architecture locate missing children?

The proposed architecture uses deep learning techniques to locate missing children. It involves a public site where users can upload images of suspicious children along with relevant information. The system then compares the uploaded photo with images of missing children in a repository. A facial image is used to train a deep learning network, which categorizes the input child and selects the closest match from the missing children dataset. The accuracy of the system is evaluated using algorithms like CNN, RESNET 50, and VGG 16. The deep learning framework addresses limitations of other biometric systems, making it a more cost-effective and reliable method for locating missing children.

Citations

Journal Article•10.1109/icait61638.2024.10690354

Harnessing Deep Learning for Missing Child Identification and Recognition

Saritha Suvarna, +4 more

- 24 Jul 2024

TL;DR: This research proposes a deep learning-based method for missing child identification using convolutional neural networks (CNNs) and large-scale face datasets, achieving exceptional accuracy in facial trait analysis, with implications for child safety and responsible technology deployment.

...read moreread less

References

•Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

Karen Simonyan, +1 more

- 04 Sep 2014

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

...read moreread less

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Proceedings Article•10.1109/ICIP.2009.5413956

Face recognition using sift features

Cong Geng, +1 more

- 07 Nov 2009

TL;DR: Two new approaches are proposed: Volume-SIFT (VSIFT) and Partial-Descriptor-Sift (PDSIFT) for face recognition based on the original SIFT algorithm, which can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.

...read moreread less

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Missing Child Identification using Convolutional Neural Network

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Most frequently asked questions

1. What are the main reasons for children disappearing in India?

2. How does the proposed architecture locate missing children?

Citations

Harnessing Deep Learning for Missing Child Identification and Recognition

References

Very Deep Convolutional Networks for Large-Scale Image Recognition

Face recognition using sift features

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