489 research outputs found

    Pattern mining approaches used in sensor-based biometric recognition: a review

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    Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

    Human brain distinctiveness based on EEG spectral coherence connectivity

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    The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherencebased connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match score fusio

    Smart Brain Interaction Systems for Office Access and Control in Smart City Context

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    Over the past decade, the term “smart cities” has been worldwide priority for city planning by governments. Planning smart cities implies identifying key drivers for transforming into more convenient, comfortable, and safer life. This requires equipping the cities with appropriate smart technologies and infrastructure. Smart infrastructure is a key component in planning smart cities: smart places, transportation, health and education systems. Smart offices present the concept of workplaces that respond to user’s needs and allow less commitment to routine tasks. Smart offices solutions enable employees to change status of the surrounding environment upon the change of user’s preferences using the changes in the user’s biometrics measures. Meanwhile, smart office access and control through brain signals is quite recent concept. Hence, smart offices provide access and services availability at each moment using smart personal identification (PI) interfaces that responds only to the personal thoughts/preferences issued by the office employee not any other person. Hence, authentication and control systems could benefit from the biometrics. Yet these systems are facing efficiency and accessibility challenges in terms of unimodality. This chapter addresses those problems and proposes a prototype for multimodal biometric person identification control system for smart office access and control as a solution

    Retina Based Glowworm Swarm Optimization for Random Cryptographic Key Generation

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    ان توليد المفاتيح المستندة إلى المقاييس الحيوية يمثل استخدام الميزات المستخرجة من السمات التشريحية (الفسيولوجية) البشرية مثل بصمات الأصابع أو شبكية العين أو السمات السلوكية مثل التوقيع. تتميز القياسات الحيوية لشبكية العين بمتانة متأصلة، وبالتالي، فهي قادرة على توليد مفاتيح عشوائية بمستوى أمان أعلى مقارنة مع السمات الحيوية الأخرى. في السنوات الأخيرة ، اكتسبت خوارزميات التحسين المستوحاة من الطبيعة شعبية كبيرة في معالجة المشكلات الواقعية الصعبة وحل وظائف التحسين المعقدة التي لا تتوفر فيها الحلول الفعلية. في هذه الورقة ، تم اقتراح نظام فعال لتوليد مفاتيح عشوائية آمنة وقوية وفريدة من نوعها تستند إلى ميزات شبكية العين لتطبيقات التشفير. يتم استخراج ميزات شبكية العين باستخدام خوارزمية تحسين سرب الدودة المتوهجة (GSO)  والتي توفر نتائج واعدة من خلال التجارب باستخدام قواعد بيانات شبكية العين القياسية. بالإضافة إلى ذلك، من أجل توفير مفاتيح عشوائية عالية الجودة وغير متوقعة وغير مجددة، تم استخدام الخريطة الفوضوية في النظام المقترح. حيث يتضمن النظام المقترح أربع مراحل رئيسية: التقاط صورة شبكية العين باستخدام أي كاميرا شبكية موجودة في الأسواق, أو باستخدام قاعدة البيانات المتاحة والتي تسمى DRIONS-DB,  ثم معالجتها معالجة اولية، ثم فصل صورة شبكية العين المعالجة مسبقًا إلى أربعة أجزاء باستخدام تحويل مويجات الهار المنفصلة ذات المستوى الواحد (DWHT), بعد ذلك ، يتم استخدام النطاق الفرعي ذو التردد المنخفض (LL) للمرحلة التالية حيث يمثل النطاق الفرعي التشغيلي, بعد ذلك ، يتم استخراج الميزات المثلى باستخدام خوارزمية تحسين سرب الدودة المتوهجة (GSO)، وأخيرًا يتم دمج الميزات المثلى مع الخريطة الفوضوية لإنشاء مفتاح التشفير العشوائي. في النتائج التجريبية، تم استخدام التحليل الإحصائي NIST الذي يتضمن عشرة اختبارات إحصائية للتحقق من عشوائية مفتاح البت الثنائي المولد. مفاتيح التشفير العشوائية التي تم الحصول عليها كانت ناجحة في اختبارات التحليل الإحصائي NIST ، بالإضافة إلى درجة كبيرة من اللامركزية.The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order to provide high-quality random, unpredictable, and non-regenerated keys, the chaotic map has been used in the proposed system. In the experiments, the NIST statistical analysis which includes ten statistical tests has been employed to check the randomness of the generated binary bits key. The obtained random cryptographic keys are successful in the tests of NIST, in addition to a considerable degree of aperiodicity

    An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

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    Background: Brain responds in a short timeframe (with certain delay) after the request for doing a motor imagery task and therefore it is most likely that the individual not focus continuously on the task at entire interval of data acquisition time or even think about other things in a very short time slice. In this paper, an effective brain-computer interface system is presented based on the optimal timeframe selection of brain signals.Methods: To prove the stated claim, various timeframes with different durations and delays selected based on a specific rule from EEG signals recorded during right/left hand motor imagery task and subsequently, feature extraction and classification are done.Results: Implementation results on the two well-known datasets termed Graz 2003 and Graz 2005; shows that the smallest systematically created timeframe of data acquisition interval have had the best results of classification. Using this smallest timeframe, the classification accuracy increased up to 91.43% for Graz 2003 and 88.96, 83.64 and 84.86 percent for O3, S4 and X11 subjects of Graz 2005 database respectively.Conclusion: Removing the additional information in which the individual does not focus on the motor imagery task and utilizing the most distinguishing timeframe of EEG signals that correctly interpret individual intentions improves the BCI system performance

    Biometric authentication using the PPG: A long-term feasibility study

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    The photoplethysmogram (PPG) is a biomedical signal that can be used to estimate volumetric blood flow changes in the peripheral circulation. During the past few years, several works have been published in order to assess the potential for PPGs to be used in biometric authentication systems, but results are inconclusive. In this paper we perform an analysis of the feasibility of using the PPG as a realistic biometric alternative in the long term. Several feature extractors (based on the time domain and the Karhunen–Loève transform) and matching metrics (Manhattan and Euclidean distances) have been tested using four different PPG databases (PRRB, MIMIC-II, Berry, and Nonin). We show that the false match rate (FMR) and false non-match rate (FNMR) values remain constant in different time instances for a selected threshold, which is essential for using the PPG for biometric authentication purposes. On the other hand, obtained equal error rate (EER) values for signals recorded during the same session range from 1.0% for high-quality signals recorded in controlled conditions to 8% for those recorded in conditions closer to real-world scenarios. Moreover, in certain scenarios, EER values rise up to 23.2% for signals recorded over different days, signaling that performance degradation could take place with time

    Scalable Digital Architecture of a Liquid State Machine

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    Liquid State Machine (LSM) is an adaptive neural computational model with rich dynamics to process spatio-temporal inputs. These machines are extremely fast in learning because the goal-oriented training is moved to the output layer, unlike conventional recurrent neural networks. The capability to multiplex at the output layer for multiple tasks makes LSM a powerful intelligent engine. These properties are desirable in several machine learning applications such as speech recognition, anomaly detection, user identification etc. Scalable hardware architectures for spatio-temporal signal processing algorithms like LSMs are energy efficient compared to the software implementations. These designs can also naturally adapt to dierent temporal streams of inputs. Early literature shows few behavioral models of LSM. However, they cannot process real time data either due to their hardware complexity or xed design approach. In this thesis, a scalable digital architecture of an LSM is proposed. A key feature of the architecture is a digital liquid that exploits spatial locality and is capable of processing real time data. The quality of the proposed LSM is analyzed using kernel quality, separation property of the liquid and Lyapunov exponent. When realized using TSMC 65nm technology node, the total power dissipation of the liquid layer, with 60 neurons, is 55.7 mW with an area requirement of 2 mm^2. The proposed model is validated for two benchmark. In the case of an epileptic seizure detection an average accuracy of 84% is observed. For user identification/authentication using gait an average accuracy of 98.65% is achieved
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