scholarly journals Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1046 ◽  
Author(s):  
Abeer D. Algarni ◽  
Ghada M. El Banby ◽  
Naglaa F. Soliman ◽  
Fathi E. Abd El-Samie ◽  
Abdullah M. Iliyasu
Keyword(s):  
Face Recognition ◽  
Characteristic Curve ◽  
Similarity Index ◽  
Random Projection ◽  
Personal Identification ◽  
Random Projections ◽  
Person Identification ◽  
Security Systems ◽  
Biometric Data ◽  
Rich Diversity

To circumvent problems associated with dependence on traditional security systems on passwords, Personal Identification Numbers (PINs) and tokens, modern security systems adopt biometric traits that are inimitable to each individual for identification and verification. This study presents two different frameworks for secure person identification using cancellable face recognition (CFR) schemes. Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits. In the first framework, a hybrid structure combining Intuitionistic Fuzzy Logic (IFL) with RP is used to accomplish full distortion and encryption of the original biometric traits to be saved in the database, which helps to prevent unauthorised access of the biometric data. The framework involves transformation of spatial-domain greyscale pixel information to a fuzzy domain where the original biometric images are disfigured and further distorted via random projections that generate the final cancellable traits. In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits. Here, the use of reflectance properties is motivated by its ability to retain most image details, while the guarantee of the non-invertibility of the cancellable biometric traits supports the rationale behind our utilisation of another RP stage in both frameworks, since independent outcomes of both the IFL stage and the reflectance component of the homomorphic transform are not enough to recover the original biometric trait. Our CFR schemes are validated on different datasets that exhibit properties expected in actual application settings such as varying backgrounds, lightings, and motion. Outcomes in terms standard metrics, including structural similarity index metric (SSIM) and area under the receiver operating characteristic curve (AROC), suggest the efficacy of our proposed schemes across many applications that require person identification and verification.

Naturalistic Male Skateboard User Speed Study

2020 ◽  
Author(s):  
Sean Buczek ◽  
Lauren Eichaker ◽  
Troy Graham ◽  
Thomas Maull
Keyword(s):  
Traffic Safety ◽  
Sport Participation ◽  
User Behavior ◽  
Public Spaces ◽  
Travel Speed ◽  
Personal Identification ◽  
Accident Reconstruction ◽  
Biometric Data ◽  
Useful Life ◽  
Bearing Design

Abstract Skateboards have been used as a means of transportation and extreme sport participation for decades. However, the prevalence of skateboards as a source of transportation is increasing. The laws that permit skateboard users to travel in roadways and in pedestrian walkways can vary by state, city, or county, allowing for a large variance in travel speed and user behavior. The amount of data available for the average speed of skateboard users during travel and trick initiation is limited. This study will preliminarily describe the natural travel and trick initiation speeds of skateboard users. The data that is presented in this study is beneficial to a vast audience including, but not limited to: traffic safety, road and intersection design, accident reconstruction, skateboard design, bearing design and useful life, and wheel design and useful life. This is an observational study of users on public spaces; no personal identification or biometric data was collected.

Biometric Security

2015 ◽  
pp. 199-217
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

scholarly journals Shuffle block SRGAN for face image super-resolution reconstruction

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1429-1439
Author(s):  
Ziwei Zhang ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Hongfei Kan ◽  
Juan Wen
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Super Resolution ◽  
Face Image ◽  
Low Resolution ◽  
Index Method ◽  
Face Features ◽  
Image Super Resolution

When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals A Cancelable Iris- and Steganography-Based User Authentication System for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2985 ◽  
Author(s):  
Wencheng Yang ◽  
Song Wang ◽  
Jiankun Hu ◽  
Ahmed Ibrahim ◽  
Guanglou Zheng ◽  
...  
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
System Security ◽  
Random Projection ◽  
Biometric Data ◽  
Iot Security ◽  
Biometric Systems ◽  
Authentication System ◽  
Iot Devices ◽  
The Internet Of Things

Remote user authentication for Internet of Things (IoT) devices is critical to IoT security, as it helps prevent unauthorized access to IoT networks. Biometrics is an appealing authentication technique due to its advantages over traditional password-based authentication. However, the protection of biometric data itself is also important, as original biometric data cannot be replaced or reissued if compromised. In this paper, we propose a cancelable iris- and steganography-based user authentication system to provide user authentication and secure the original iris data. Most of the existing cancelable iris biometric systems need a user-specific key to guide feature transformation, e.g., permutation or random projection, which is also known as key-dependent transformation. One issue associated with key-dependent transformations is that if the user-specific key is compromised, some useful information can be leaked and exploited by adversaries to restore the original iris feature data. To mitigate this risk, the proposed scheme enhances system security by integrating an effective information-hiding technique—steganography. By concealing the user-specific key, the threat of key exposure-related attacks, e.g., attacks via record multiplicity, can be defused, thus heightening the overall system security and complementing the protection offered by cancelable biometric techniques.

Risking identity: a case study of Jamaica’s short-lived national ID system

2020 ◽  
Vol 18 (3) ◽  
pp. 329-338
Author(s):  
Hopeton S. Dunn
Keyword(s):  
Face Recognition ◽  
Data Collection ◽  
Data Storage ◽  
Public Consultation ◽  
Social Implications ◽  
Extensive Literature ◽  
Biometric Data ◽  
Content Type ◽  
Social Challenges

Purpose This paper aims to expose the challenges facing the attempt by Jamaica to introduce a new digital ID system without adequate regard to public consultation and the rights of citizens. Design/methodology/approach The method used is critical text analysis and policy analysis, providing background and relevant factors leading up to the legislative changes under review. Extensive literature sources were consulted and the relevant sections of the Jamaican constitution referenced and analysed. Findings The case study may have national peculiarities not applicable in other jurisdictions. Its introduction acknowledges that the Jamaican Government may amend and re-submit the legislation, absent the flawed clauses. The paper however will remain valid given its detailed analysis and exposure of risks associated with biometric data collection, face recognition technology and data storage flaws. Practical implications It will be a practical example of the risks associated with flawed biometric data collection and the role of Courts in reviewing such legislation. Referrals to the Courts can be used as a remedy, as occurred not only in Jamaica but also in many other jurisdictions, including India and Kenya. Social implications The paper foregrounds the rights of citizens to be consulted on the collection and storage of their sensitive biometric data. The social implications and risks of violating the constitutional rights of citizens were made evident, and can be an example to other jurisdictions. Originality/value The paper is the first of its kind to provide detailed data and analysis on an outright rejection by the Courts of a country's ID legislation on grounds that it violated the constitution and rights of citizens. It shows the ethical and social challenges in proposing and implementing legislation without adequate public consultation on such sensitive matters as biometric data. It also exposes some of the challenges of artificial intelligence and face recognition technologies in ID data collection, including flaws related to race, gender and coding.

Multispectral face spoofing detection using VIS–NIR imaging correlation

2018 ◽  
Vol 16 (02) ◽  
pp. 1840003 ◽  
Author(s):  
Xudong Sun ◽  
Lei Huang ◽  
Changping Liu
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Experimental Results ◽  
Security Systems ◽  
Local Descriptors ◽  
Confidence Factor ◽  
Nir Imaging ◽  
Spoofing Detection ◽  
Factor Map ◽  
Face Spoofing

With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.

scholarly journals Random projections and Hotelling’s T2 statistics for change detection in high-dimensional data streams

2013 ◽  
Vol 23 (2) ◽  
pp. 447-461 ◽  
Author(s):  
Ewa Skubalska-Rafajłowicz
Keyword(s):  
Discrete Time ◽  
Data Streams ◽  
Random Projection ◽  
Image Sequences ◽  
High Dimensional ◽  
Random Projections ◽  
Random Subspace ◽  
Noisy Image ◽  
Method Of Dimensionality Reduction ◽  
Diagnostic Properties

The method of change (or anomaly) detection in high-dimensional discrete-time processes using a multivariate Hotelling chart is presented. We use normal random projections as a method of dimensionality reduction. We indicate diagnostic properties of the Hotelling control chart applied to data projected onto a random subspace of Rn. We examine the random projection method using artificial noisy image sequences as examples.

scholarly journals Dimensionality reduction for k-distance applied to persistent homology

2021 ◽  
Author(s):  
Shreya Arya ◽  
Jean-Daniel Boissonnat ◽  
Kunal Dutta ◽  
Martin Lotz
Keyword(s):  
Dimensionality Reduction ◽  
Persistent Homology ◽  
Random Projection ◽  
Distance Functions ◽  
Random Projections ◽  
Annual Symposium ◽  
Gaussian Width ◽  
College Park ◽  
Low Dimensional ◽  
Open Question

AbstractGiven a set P of n points and a constant k, we are interested in computing the persistent homology of the Čech filtration of P for the k-distance, and investigate the effectiveness of dimensionality reduction for this problem, answering an open question of Sheehy (The persistent homology of distance functions under random projection. In Cheng, Devillers (eds), 30th Annual Symposium on Computational Geometry, SOCG’14, Kyoto, Japan, June 08–11, p 328, ACM, 2014). We show that any linear transformation that preserves pairwise distances up to a $$(1\pm {\varepsilon })$$ ( 1 ± ε ) multiplicative factor, must preserve the persistent homology of the Čech filtration up to a factor of $$(1-{\varepsilon })^{-1}$$ ( 1 - ε ) - 1 . Our results also show that the Vietoris-Rips and Delaunay filtrations for the k-distance, as well as the Čech filtration for the approximate k-distance of Buchet et al. [J Comput Geom, 58:70–96, 2016] are preserved up to a $$(1\pm {\varepsilon })$$ ( 1 ± ε ) factor. We also prove extensions of our main theorem, for point sets (i) lying in a region of bounded Gaussian width or (ii) on a low-dimensional submanifold, obtaining embeddings having the dimension bounds of Lotz (Proc R Soc A Math Phys Eng Sci, 475(2230):20190081, 2019) and Clarkson (Tighter bounds for random projections of manifolds. In Teillaud (ed) Proceedings of the 24th ACM Symposium on Computational Geom- etry, College Park, MD, USA, June 9–11, pp 39–48, ACM, 2008) respectively. Our results also work in the terminal dimensionality reduction setting, where the distance of any point in the original ambient space, to any point in P, needs to be approximately preserved.

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