AI for Detection of Missing Person
Identifying missing people and bringing them back to their families has become a universal issue. Various research publications are examined in this paper. Each of the existing mechanisms has merits and demerits. But the issues related to bringing back missing people have not been perfected 100% Com...
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Published in: | 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) pp. 66 - 73 |
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Main Authors: | , , , |
Format: | Conference Proceeding |
Language: | English |
Published: |
IEEE
09-05-2022
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Subjects: | |
Online Access: | Get full text |
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Summary: | Identifying missing people and bringing them back to their families has become a universal issue. Various research publications are examined in this paper. Each of the existing mechanisms has merits and demerits. But the issues related to bringing back missing people have not been perfected 100% Computing technology has evolved in recent years to include a wide range of flavors that can be used in practically every sector. Information plays a critical function in the computing system, notwithstanding the rapid-fire rise of technology. Every day, a considerable number of people, including children, teenagers, mentally challenged people, elderly people with Alzheimer's disease, and others, go missing around the world. In India, more than 500 missing person concerns are estimated to go unaddressed every day. Face recognition technologies have become increasingly important in recent decades. A facial recognition system is a computer application that can recognize or verify a person by analyzing a digital image or a video frame from a video source. Facial feature detection and recognition are extensively used in current world scenarios and technologies. Artificial intelligence, on the other hand, has given solutions to the issues of the ultramodern world. Artificial intelligence (AI) has been developed to help humans and machines communicate more effectively. The proposed mechanism has been successfully implemented to accurately identify a face with a precision of 90% when compared to 59% using KNN and 43% using SVM with PCA. |
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DOI: | 10.1109/ICAAIC53929.2022.9792672 |