IoT-Enabled Multimodal Biometric Recognition System in Secure Environment

Abstract

A multimodal biometric recognition system on Internet of Things (IoT) with Blockchain environments has been proposed in this paper. This system distributes a decentralized biometric authentication process mechanism and improves security in the IoT environment. The implementation of this system consists of six components: image preprocessing, feature representation, cancelable biometrics, classification, and encryption-decryption of multimodal biometrics templates. A region of interest is segmented from each biometric trait during image preprocessing. Then a discriminant feature extraction technique has been employed for feature computation. A cancelable biometric system is introduced to secure and preserve the original biometric features from external hazards and misuse. The extracted cancelable features undergo classification to perform the subjects&#x2019

authentication. Then, a method of encryption-decryption of templates is performed to handle the various online authentication attacks and improve IoT-enabled authentication. Finally, the recognition scores due to iris, periocular, palmprint, and face biometrics, are fused to increase the performance of the proposed IoT-enabled multimodal biometric system. The proposed system obtains identification performance 99.92%, 100% for CASIA-v4-distance, UBIRIS-v2 iris, 100% for periocular (CASIA-v4-distance, UBIRIS-v2), 100% for Bosphorus palmprint, and 100% for FERET face databases using 30-dimensional cancelable features that show the superiority of the proposed system as compared with state-of-the-art methods. IEEE