Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

Encryption by Heart (EbH)-Using ECG for time-invariant symmetric key generation

Wearable devices are a part of Internet-of-Things (IoT) that may offer valuable data of their porting user. This paper explores the use of ElectroCardioGram (ECG) records to encrypt user data. Previous attempts have shown that ECG can be taken as a basis for key generation. However, these approaches do not consider time-invariant keys. This feature enables using these so-created keys for symmetrically encrypting data (e.g. smartphone pictures), enabling their decryption using the key derived from the current ECG readings. This paper addresses this challenge by proposing EbH, a mechanism for persistent key generation based on ECG. EbH produces seeds from which encryption keys are generated. Experimental results over 24 h for 199 users show that EbH, under certain settings, can produce permanent seeds (thus time-invariant keys) computed on-the-fly and different for each user up to 95.97% of users produce unique keys. In addition, EbH can be tuned to produce seeds of different length (up to 300 bits) and with variable min-entropy (up to 93.51). All this supports the workability of EbH in a real setting. (C) 2017 Elsevier B.V. All rights reserved.Funding: This work was supported by the MINECO grants TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and TIN2016-79095-C2-2-R (SMOG-DEV); by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks), which is co-funded by European Funds (FEDER); and by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid, Spain (J. M. de Fuentes and L. Gonzalez-Manzano grants). Data used for this research was provided by the Telemetric and ECG Warehouse (THEW) of University of Rochester, NY