Adoghe, A. U. and Noma-Osaghae, Etinosa and Okokpujie, Kennedy O. and Edose, Uduebholo Emmanuel (2021) A HAAR CASCADE CLASSIFIER BASED DEEP-DATASET FACE RECOGNITION ALGORITHM FOR LOCATING MISS-ING PERSONS. Journal of Theoretical and Applied Information Technology, 99 (18). ISSN 1817-3195
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Abstract
A countless number of persons, including children, teenagers, adults, and mentally challenged peo-ple, go missing every day. Some are victims of kidnap and human trafficking, while others got miss-ing in unfamiliar places. Effectively identifying people has always been a fascinating subject, both in industry and research. The majority of proposed solutions have not considered the possibility of using cameras in public places for detecting the faces of missing persons in real-time. Therefore, this paper presents implementing a Haar Cascade Classifier Based Deep-Dataset Face Recognition Algorithm on cameras to locate missing persons in public and notify law enforcement of missing persons found. This research study employs the in-depth learning approach using Open Computer Vision to automate searching for missing persons using public cameras, thereby improving security, safety, and reducing the time taken to find missing persons. The implemented system is the solution to a closed-set problem where the proposed algorithm assumes a deep dataset gallery of the trained face image of missing persons. The real-time implementation of the trained face recognition algo-rithm gave an average experimental accuracy of 72.9%.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Face Recognition, Missing People, Haar Cascade Classifier, OpenCV, Deep Dataset, Rasberry Pi |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | nwokealisi |
| Date Deposited: | 27 Jul 2023 12:57 |
| Last Modified: | 27 Jul 2023 12:57 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/17211 |
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