Anti-Spoofing for Fingerprint Recognition Using Electric Body Pulse Response

• Published in: IEEE internet of things journal Vol. 11; no. 4; pp. 5993 - 6006
• Main Authors: Kang, Taewook, Oh, Kwang-Il, Lee, Jae-Jin, Kim, Sung-Eun, Lee, Woojoo, Oh, Wangrok, Kim, Seong-Eun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Anomalies; Anomaly detection; Biometric recognition systems; Business process re-engineering; Circuits; Couplings; Cybersecurity; Detection algorithms; electric pulse response; Electric variables measurement; Errors; Feature extraction; fingerprint anti-spoofing; fingerprint authentication; Fingerprint recognition; Fingerprint verification; Internet of Things; liveness detection; Machine learning; Principal components analysis; Redundancy; Sampling; Spoofing; Support vector machines
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3308654

This study presents a highly reliable approach to prevent fingerprint spoofing attacks based on electric body pulse responses (BPRs) in personal Internet of Things (IoT) gadgets. Real fingerprint pulse response (RFPR) and fake fingerprint pulse response (FFPR) data were collected from ten subjects for four weeks. The FFPR was obtained by wearing a fake fingerprint made of artificial substances, such as conductive silicone, over the finger. We analyzed different patterns of FFPR compared to RFPR using an electric circuit model of the proposed fingerprint anti-spoofing system based on BPRs. Simple features comprising ten, five, or three datapoints were selected by the minimum redundancy maximum relevance (MRMR) algorithm and led to reduction in processing complexity. We also validated its robustness to sampling offset errors caused by practical sampling operations in devices based on the evaluation of classification accuracy using machine learning algorithms, such as <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-nearest neighbor (KNN) and support vector machine (SVM). Finally, the effectiveness of the selected feature was evaluated using unsupervised anomaly detection algorithms, such as principal component analysis (PCA), one-class SVM (OC-SVM), and variational autoencoder (VAE), in a practical scenario with sampling offset errors in the training and test data. The VAE outperformed PCA and OC-SVM by achieving a detection accuracy of 99.76% using raw data under 100 datapoints and 97.60% with reduced features having only five datapoints, regardless of sampling offset errors. Therefore, the proposed anomaly detection system based on EPRs can provide promising fingerprint spoof detection in IoT devices with limited computing resources.