IoT-Enabled Multimodal Biometric Recognition System in Secure Environment

Saiyed Umer, Alamgir Sardar, Ranjeet Kumar Rout, M. Tanveer, Imran Razzak

Abstract: A multimodal biometric recognition system on Internet of Things (IoT) with Blockchain environments has been proposed in this article. This system distributes a decentralized biometric authentication process mechanism and improves security in the IoT environment. The implementation of this system consists of five components: 1) image preprocessing; 2) feature representation; 3) cancelable biometrics; 4) classification; and 5) 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 cancellable biometric system (CBS) 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’ 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 (CASIA-DIST), UBIRIS-v2 iris, 100% for periocular (CASIA-DIST, UBIRIS-v2), 100% for Bosphorus palmprint, and 100% for FERET face databases using 30-D cancelable features that show the superiority of the proposed system as compared with state-of-the-art methods.

External IDs:doi:10.1109/jiot.2023.3299465