A Recurrent Neural Network on Video-Based Face Identification Suspicious and Also Wanted People Recognition in ATM

DOI

https://www.doi.org/10.59256/indjcst.20240302021

Published In

Volume 3, Issue 2 (2024)

Published Online

May-August 2024

Pages

140-147

Article Type

Research Article

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The need for an intelligent recognition system is still increasing. Traditional approaches based on access to restricted places like ATM or suspected actions as theft, scam, and loitering. They are insufficient to identify suspects. These actions do not represent a real key of suspects. This project is motivated not only by the limits of the traditional approaches but also by the complexity of Intelligent Algorithms (RNN). In this project, we propose an approach for the automatic comparative labeling of facial soft biometric. The three main categories wanted persons, regular customers, and new persons serve distinct purposes. For wanted people recognition, the system is trained to identify individuals with outstanding or flagged in law enforcement databases. Regular ATM customers are recognized based on their registered facial features. New persons, on the other hand, are those not previously registered, and the system is designed to capture their facial data for future reference. Using a subset from the RNN-network datasets, recurrent neural network our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes. Recurrent Neural Network (RNN) system can be implement on CCTV cameras and it will be alerted particular officers. The application is easy to use and it is equipped with pyttsx3 so that the face detected is communicated to the people as voice alert.