"A FALL DETECTION AND PREVENTION SYSTEM FOR ELDERLY SAFETY IN SMART HOMES USING DEEP LEARNING"

Abstract

Particularly in administrative, felony, and financial settings, the legitimacy of handwritten signatures is still an crucial element of private identity and document validation. However, human mistake can arise for the duration of manual verification, that's why automated systems which could reliably differentiate between true and faux signatures are getting an increasing number of necessary. Using photo-based information, In order to distinguish between genuine and fraudulent signatures, this research suggests a Convolutional Neural Network (CNN)-based method. The CNN architecture turned into created to improve accuracy and dependability through routinely getting to know spatial hierarchies of signature traits without the need for guide preprocessing. A publicly handy online signature dataset was used to educate and compare the version; every picture turned into superior and normalized to enhance generalization. According to experimental information, the suggested CNN version efficiently recognized both true and fake signatures with high precision and keep in mind fees, with an usual accuracy of over ninety five%. The model's potential to lessen inaccurate classifications was proven by way of the performance assessment using the confusion matrix and F1-rankings. This study advances the realm of biometric verification. by using organising a strong and scalable framework for automated signature verification, suitable for deployment in secure virtual identity systems.