International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
SURVEY ON MISSING CHILD IDENTIFICATION Dr.RaviKanth Motupalli, Aredla Likitha Reddy,Billakanti Sushma,Komarabathini Kavya,Chidugulla Poojitha 1Assistant professor, Dept. Of Computer Science Engineering, VNRVJIET college, Telangana, India 2Student, Dept. Of Computer Science Engineering, VNRVJIET college, Telangana ,India
3Student, Dept. Of Computer Science Engineering, VNRVJIET college, Telangana, India 4Student, Dept. Of Computer Science Engineering, VNRVJIET college, Telangana, India 5Student, Dept. Of Computer Science Engineering, VNRVJIET college, Telangana, India
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Abstract - In India many countless numbers of children are
difficult.There are many papers focussed on missing child identification but most of them focussed on Matching the child’s photo to the database but most of the real issues were hidden.In this survey paper we will discuss the issues of existing missing child identification.
reported missing every year. Among those missing child cases a large number of children remain untraced. Many NGOs estimate that the number of children missing is much higher than reported. The missing child from one region may be found in another state or another country, for various reasons. So even if a child is found, it is difficult to identify that child from the reported missing cases.
2. LITERATURE SURVEY G.Alekya [1] project aims to utilize AI, including Haar-Cascade and LBPH face recognition, to identify missing children from CCTV footage in real-time. It creates a unique dataset, linking children's images with IDs, facilitating quick and accurate recognition in public areas to aid law enforcement. The conclusion is based on the successful development of an AI model using Haar-Cascade and LBPH for recognizing missing children from CCTV footage, ensuring efficient identification in real-time scenarios with accuracy of 95% . Future enhancements could include integrating deep learning models for better face recognition, real-time tracking, and integrating with databases for faster child identification.
In the present scenario, to find the missing child many technologies and methods have been introduced like using Face Recognition technology, through mobile apps etc. Close to 3,000 missing children have been found in New Delhi to the implementation of an experimental Face Recognition Software System. Using Mobile apps by sending notifications 611 missing children have been saved in China. So in this survey we discuss more about techniques for finding missing children. Key Words: Missing child, Face Recognition System, Generative Adversarial Networks, FaceNet, Style GAN, Age Progression.
M. Raghavendra, R. Neha , S. Manasa, Asst Prof. Mrs. A.V Lakshmi Prasuna[2] The objectives include assessing CNN models' performance in locating missing children, evaluating accuracy, and testing robustness to various image conditions. A CNN-based approach using VGG16 and ResNet-50 architectures is employed to identify missing children by comparing user-uploaded images with a dataset, demonstrating promising potential. Future extensions of the CNN-based missing children identification system could include real-time facial recognition, age progression modeling, and integration with law enforcement databases for enhanced accuracy and efficiency.
1.INTRODUCTION A "missing child" is any child who is lost (separated from family), has been abducted, kidnapped, trafficked, or abandoned, or who has left home on their own without warning.Usually, parents/ family/ guardians file a missing complaint in such cases.According to recent studies, a significant number of youngsters go missing every year. This is primarily higher in India's scenario. Most of the parents file a police complaint, upload images in social media, attach posters on walls, search nearby places for their children. These methods may be effective if the child is in the nearby area but if the child has gone to a distant area because of trafficking or kidnapping then the tracing of the child would become difficult. It may even take many years to trace the child. Identifying the child after many years is a challenge. This comes with another challenge where extracting the facial features of very small children who are missing at the age of 1-5 years is very
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D.J. Naidu1, R. Lokesh [3] The objective is to enhance missing children identification using a CNN-based feature extraction and SVM classification system, achieving robust recognition under diverse conditions. Incorporating SVMs with CNN-based feature extraction enhances child identification, achieving 99.41% accuracy, demonstrating robustness for missing children recognition in varied conditions. Future enhancements: Implement real-time video analysis, integrate with social media for wider data
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