Neural Networks for Fingerprint Recognition

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed

A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes

Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.Ministerio de Economía, Industria y Competitividad TEC2014-57971-R, TEC2017-83557-

Integrated Circuit Wear-out Prediction and Recycling Detection using Radio-Frequency Distinct Native Attribute Features

Radio Frequency Distinct Native Attribute (RF-DNA) has shown promise for detecting differences in Integrated Circuits(IC) using features extracted from a devices Unintentional Radio Emissions (URE). This ability of RF-DNA relies upon process variation imparted to a semiconductor device during manufacturing. However, internal components in modern ICs electronically age and wear out over their operational lifetime. RF-DNA techniques are adopted from prior work and applied to MSP430 URE to address the following research goals: 1) Does device wear-out impact RF-DNA device discriminability?, 2) Can device age be continuously estimated by monitoring changes in RF-DNA features?, and 3) Can device age state (e.g., new vs. used) be reliably estimated? Conclusions include: 1) device wear-out does impact RF-DNA, with up to a 16 change in discriminability over the range of accelerated ages considered, 2) continuous(hour-by-hour) age estimation was most challenging and generally not supported, and 3) binary new vs. used age estimation was successful with 78.7 to 99.9 average discriminability for all device-age combinations considered

Multivariate Stochastic Approximation to Tune Neural Network Hyperparameters for Criticial Infrastructure Communication Device Identification

The e-government includes Wireless Personal Area Network (WPAN) enabled internet-to-government pathways. Of interest herein is Z-Wave, an insecure, low-power/cost WPAN technology increasingly used in critical infrastructure. Radio Frequency (RF) Fingerprinting can augment WPAN security by a biometric-like process that computes statistical features from signal responses to 1) develop an authorized device library, 2) develop classifier models and 3) vet claimed identities. For classification, the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is employed. GRLVQI has shown high fidelity in classifying Z-Wave RF Fingerprints; however, GRLVQI has multiple hyperparameters. Prior work optimized GRLVQI via a full factorial experimental design. Herein, optimizing GRLVQI via stochastic approximation, which operates by iterative searching for optimality, is of interest to provide an unconstrained optimization approach to avoid limitations found in full factorial experimental designs. The results provide an improvement in GRLVQI operation and accuracy. The methodology is further generalizable to other problems and algorithms

Tuning Hyperparameters for DNA-based Discrimination of Wireless Devices

The Internet of Things (IoT) and Industrial IoT (IIoT) is enabled by Wireless Personal Area Network (WPAN) devices. However, these devices increase vulnerability concerns of the IIoT and resultant Critical Infrastructure (CI) risks. Secure IIoT is enabled by both pre-attack security and post-attack forensic analysis. Radio Frequency (RF) Fingerprinting enables both pre- and post-attack security by providing serial-number level identification of devices through fingerprint characterization of their emissions. For classification and verification, research has shown high performance by employing the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier. However, GRLVQI has numerous hyperparameters and tuning requires AI expertise, thus some researchers have abandoned GRLVQI for notionally simpler, but less accurate, methods. Herein, we develop a fool-proof approach for tuning AI algorithms. For demonstration, Z-Wave, an insecure low-power/cost WPAN technology, and the GRLVQI classifier are considered. Results show significant increases in accuracy (5% for classification, 50% verification) over baseline methods

Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a neural network-based approach for quantifying signal distortions in a relative distortion latent space. Coupled with a Dense network, FEDR fingerprints were evaluated against common RFF techniques for up to 100 unseen classes, where FEDR achieved best performance with MCC ranging from 0.945 (5 classes) to 0.746 (100 classes), using 73% fewer training parameters than the next-best technique