scholarly journals Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept

2011 ◽  
Vol 11 ◽  
pp. 503-519 ◽  
Author(s):  
A. Drosou ◽  
D. Ioannidis ◽  
K. Moustakas ◽  
D. Tzovaras
Keyword(s):  
State Of The Art ◽  
Wide Spectrum ◽  
Gait Recognition ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Physiological Profile ◽  
Security Authentication ◽  
Recognition Systems ◽  
Real Scenario ◽  
New Algorithms

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.

scholarly journals Human gait recognition using preprocessing and classification techniques

2020 ◽  
Vol 10 (3) ◽  
pp. 2978
Author(s):  
Samer Kais Jameel ◽  
Jihad Anwar Qadir ◽  
Mohammed Hussein Ahmed
Keyword(s):  
Gait Recognition ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Human Gait ◽  
System Efficiency ◽  
Kinect Sensor ◽  
Biometric Recognition ◽  
The People ◽  
Object Movement ◽  
Recognition Systems

Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

Multispectral Palmprint Recognition: A Survey and Comparative Study

2019 ◽  
Vol 28 (07) ◽  
pp. 1950107 ◽  
Author(s):  
Yassir Aberni ◽  
Larbi Boubchir ◽  
Boubaker Daachi
Keyword(s):  
Comparative Study ◽  
Feature Fusion ◽  
State Of The Art ◽  
Palmprint Recognition ◽  
Electromagnetic Spectrum ◽  
Multispectral Images ◽  
Biometric Recognition ◽  
Person Recognition ◽  
Recognition Systems ◽  
The Rich

Multispectral palmprint recognition has been investigated for many problems and applications over the last decade. It has become one of the most well-known biometric recognition systems. Its success is due to the rich features that can be extracted and exploited from the multispectral images of palmprint captured within specific wavelength ranges across the electromagnetic spectrum. This paper provides an overview of recent state-of-the-art multispectral palmprint approaches for person recognition. The approaches surveyed are discussed by describing, in particular, their feature extraction, feature fusion, matching and decision algorithms. Finally, a comparative study to evaluate their performances for both verification and identification modes is addressed.

Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

2021 ◽  
Vol 20 (4) ◽  
pp. 1-19
Author(s):  
Manjunath K. E. ◽  
Srinivasa Raghavan K. M. ◽  
K. Sreenivasa Rao ◽  
Dinesh Babu Jayagopi ◽  
V. Ramasubramanian
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Window Size ◽  
Recognition System ◽  
Error Rates ◽  
Indian Languages ◽  
International Phonetic Alphabet ◽  
Phone Recognition ◽  
Front End ◽  
Recognition Systems

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

scholarly journals Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

AI Magazine ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 10-21 ◽  
Author(s):  
Oriel Uzan ◽  
Reuth Dekel ◽  
Or Seri ◽  
Ya’akov (Kobi) Gal
Keyword(s):  
Learning Environments ◽  
Educational Software ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
Plan Recognition ◽  
Exploratory Learning ◽  
Domain Expert ◽  
Teaching Chemistry ◽  
Exploratory Learning Environments ◽  
New Algorithms

This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.

scholarly journals Enabling multiscale variation analysis with genome graphs

2021 ◽  
Author(s):  
Brice Letcher ◽  
Martin Hunt ◽  
Zamin Iqbal
Keyword(s):  
Genetic Variation ◽  
Directed Acyclic Graph ◽  
Structural Variation ◽  
Reference Genome ◽  
Multiple Scales ◽  
State Of The Art ◽  
Variant Calling ◽  
Variation Analysis ◽  
New Algorithms ◽  
Genome Graph

AbstractBackgroundStandard approaches to characterising genetic variation revolve around mapping reads to a reference genome and describing variants in terms of differences from the reference; this is based on the assumption that these differences will be small and provides a simple coordinate system. However this fails, and the coordinates break down, when there are diverged haplotypes at a locus (e.g. one haplotype contains a multi-kilobase deletion, a second contains a few SNPs, and a third is highly diverged with hundreds of SNPs). To handle these, we need to model genetic variation that occurs at different length-scales (SNPs to large structural variants) and that occurs on alternate backgrounds. We refer to these together as multiscale variation.ResultsWe model the genome as a directed acyclic graph consisting of successive hierarchical subgraphs (“sites”) that naturally incorporate multiscale variation, and introduce an algorithm for genotyping, implemented in the software gramtools. This enables variant calling on different sequence backgrounds. In addition to producing regular VCF files, we introduce a JSON file format based on VCF, which records variant site relationships and alternate sequence backgrounds.We show two applications. First, we benchmark gramtools against existing state-of-the-art methods in joint-genotyping 17 M. tuberculosis samples at long deletions and the overlapping small variants that segregate in a cohort of 1,017 genomes. Second, in 706 African and SE Asian P. falciparum genomes, we analyse a dimorphic surface antigen gene which possesses variation on two diverged backgrounds which appeared to not recombine. This generates the first map of variation on both backgrounds, revealing patterns of recombination that were previously unknown.ConclusionsWe need new approaches to be able to jointly analyse SNP and structural variation in cohorts, and even more to handle variants on different genetic backgrounds. We have demonstrated that by modelling with a directed, acyclic and locally hierarchical genome graph, we can apply new algorithms to accurately genotype dense variation at multiple scales. We also propose a generalisation of VCF for accessing multiscale variation in genome graphs, which we hope will be of wide utility.

Wearable Gait Recognition Systems Based on MEMS Pressure and Inertial Sensors: A Review

2021 ◽  
pp. 1-1
Author(s):  
Wenchao Li ◽  
Wenqian Lu ◽  
Xiaopeng Sha ◽  
Hualin Xing ◽  
Jiazhi Lou ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Gait Recognition ◽  
Recognition Systems

Novel Applications of Multimodal Biometrics

2013 ◽  
pp. 147-163
Keyword(s):  
Social Networks ◽  
Gait Recognition ◽  
System Security ◽  
Fine Tuning ◽  
Context Information ◽  
Behavioral Patterns ◽  
Multimodal Biometrics ◽  
Original Idea ◽  
Computationally Expensive ◽  
Novel Applications

This chapter presents the original idea of using social networks and context information in multimodal biometric for increased system security. A recently investigated study’s outcomes is presented, which showcase this idea as a new step in multi-biometric research. Since this method does not degrade the performance of the system and is not computationally expensive, it can be used in any biometric framework. However, as the amount of improvement depends on how distinctive and predictable people are in terms of their behavioral patterns, the method is most suitable for the predictable environments with some predefined behavioral routines. Fine tuning the system for each environment to find the most suitable parameters based on the behavioral patterns of that specific environment can result in better performance. This research is validated on example of gait recognition.

scholarly journals Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5523 ◽  
Author(s):  
Nada Alay ◽  
Heyam H. Al-Baity
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Identification System ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Softmax Classifier ◽  
Finger Vein ◽  
Level Fusion

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

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