Internet of Things (IoT) networks and authentication applications together are regarded as one of the most inventive technological models. Surveillance cameras and other IoT devices have captured live video footage in the outdoors. Use of facial biometrics in smartphones makes things secure and complex, which is known as system authentication. One of the difficult issues is accurately interpreting digital information in a natural setting. The noise effect introduces erratic fluctuations into the subject in the natural world, which could reduce the ability of the model to recognize objects. The proposed framework will identify facial biometric characteristics in an uncontrolled natural setting where non-symmetric random changes may exist. The goal of the proposed system is to reduce the false recognition rates (FRR) in unfavorable environmental circumstances. It transforms digital data with current surroundings of individual.