INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Lost Person Recognition poses a critical challenge in various real-world scenarios, including missing person searches, forensic investigations, and security surveillance. In this paper, we propose a novel approach leveraging machine learning techniques, particularly deep learning, to address this challenge. Our method integrates advanced convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to effectively capture spatial and temporal dependencies in the input data. We demonstrate the effectiveness of our approach through extensive experiments on benchmark datasets, achieving state-of- the-art performance in vanished person recognition tasks. Additionally, we discuss the potential applications and future directions of our proposed method in real-world scenarios.
Keywords:
Lost Person Recognition, Machine Learning, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks.
Cite Article:
"Lost Person Recognition", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.261 - 266, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404039.pdf
Downloads:
000338171
Publication Details:
Published Paper ID: IJSDR2404039
Registration ID:210710
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 261 - 266
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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