Biometric Authentication using IoT Devices Powered by Deep Learning and Encrypted Verification

Abstract

IoT devices are increasingly targeted by cyber threats, where traditional passwords fail due to phishing and reuse, and raw biometric storage risk irreversible identity loss. This paper proposes an edge-centric biometric authentication system using lightweight deep learning (quantized MobileNetV2 CNN) for feature extraction from fingerprint or face data, cancelable transformations for revocable non-invertible templates, and AES-256 encryption for privacy. On-device processing on Raspberry Pi/ESP32 reduces latency and prevents data exposure. Evaluations on benchmark datasets yield >96% accuracy, EER <2%, and robust spoofing resistance, suitable for smart homes, healthcare, and IIoT