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• W4384135042 abstract "In the Police Department, officials need to carry out regular beats in prescribed areas and monitor their attendance in real time. Marking attendance regularly at appropriate beat location at a prescribed time becomes challenging. The motive of the paper is to implement a Beat Management System using the deep metric learning algorithm for the Police Department to collaborate with the community within the assigned territory. The Deep metric learning algorithm in facial recognition systems does an excellent job in addressing the nonlinearity and scalability in comparison to the conventional algorithms. A novel solution is proposed in the form of an end-to-end application in order to overcome the challenges faced by the Police Department in registering attendance. This serves as a concrete solution to the problem with the amount of data that can be generated in the form of facial images from Police Officers along with the precision-based geolocation tracking. The result obtained shows that the model supports even smaller sample sizes of 4–5 images of everyone. Incremental dataset uses the concept of self- learning in order to train the model on more images captured, analyzed and processed continuously in real time. The system recognizes the person by matching the features with an accuracy of 99.38% for a constrained sample size." @default.
• W4384135042 created "2023-07-14" @default.
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• W4384135042 date "2023-01-01" @default.
• W4384135042 modified "2023-09-27" @default.
• W4384135042 title "Optimizing Beat Management System Using Soft Biometrics" @default.
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• W4384135042 doi "https://doi.org/10.1007/978-3-031-35641-4_20" @default.
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