Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications
مدل ترکیبی مبتنی بر باور عمیق برای سیستم بیومتریک چند حالته برای برنامه های امنیتی آینده-2021
Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological charac- teristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively.
Keywords: Multi-modal Bio-metric system | Chicken Swarm Optimization | Earthworm Optimization algorithm | Deep Belief Network | Multi-SVNN
Secure mobile internet voting system using biometric authentication and wavelet based AES
سیستم رای گیری اینترنتی تلفن همراه با استفاده از احراز هویت بیومتریک و AES مبتنی بر موجک-2021
The number of mobile phone users increases daily, and mobile devices are used for various applications like banking, e-commerce, social media, internet voting, e-mails, etc. This paper presents a secure mobile internet voting system in which a biometric method authenticates the voter. The biometric image can either be encrypted at the mobile device and send to the server or process the biometric image at the mobile device to generate the biometric template and send it to the server. The implementation of biometrics on mobile devices usually requires simplifying the algorithm to adapt to the relatively small CPU processing power and battery charge. This paper proposes a wavelet-based AES algorithm to speed up the encryption process and reduce the mobile device’s CPU utilization. The experimental analysis of three methods(AES encryption, wavelet-based AES encryption, and biometric template generation) exhibits that wavelet-based AES encryption is much better than AES encryption and template generation. The security analysis of three methods shows that AES and wavelet-based AES encryption provides better security than the biometric template’s protection. The study of the proposed internet voting system shows that biometric authentication defeats almost all the mobile-based threats.
Keywords: Internet voting | Fingerprint template | Iris code | AES encryption | Wavelet based AES encryption
An extensive survey on finger and palm vein recognition system
یک بررسی گسترده در مورد سیستم تشخیص ورید انگشت و کف دست-2021
The evolution of the internet drastically increases the utilization of online data, which needs security using unique identification. The traditional methods of security, such as password, personal identification number (PIN), could not reach the user-friendly requirement of the users. There is a necessity to provide high security for the data by using a unique identification system. Today, in many situations, biometrics plays a crucial role in today’s authentication and recognition. Biometrics deals with unique physical and behavioral characteristics. Fingerprint, face recognition, iris/ retinal recognition, voice recognition and vein recognition are some of the biometrics which is used for authentication purpose. In this paper, we highlight different processes that involve finger veins and palm veins recognition and authentication. These finger and palm vein is identified as one of the important unique identifications that can be used for the authentication which provide the security for the important personal data. In this study, we noticed that the advantage of using a vein for the authentication process is that it provides high security because it can be detected even when a finger or palm got injured, and it works only when the person is alive otherwise the authentication fails.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research – 2019.
Keywords: Authentication | Biometrics | Finger recognition | Palm vein recognition | Personal identification number
oReview on fingerprint-based identification system
مرور سیستم شناسایی مبتنی بر اثر انگشت-2021
The Biometric fingerprints are the widely utilized personal recognition tool because of their uniqueness, reliability and individuality. The fingerprint images consist of a design of the canyon & corrugation on human’s fingertips. Fingerprint validation is perhaps the most experienced methods for every biometric technique that has been rigorously substantiate through several applications. Every human being recognition methods using fingerprints are depending on one of the 3 methods: hybrid, correlation-based and Minutiae-based. This paper gives the review of different fingerprint recognition methods & then discusses the general minutiae-depend fingerprint identification systems. In present time the best form of recognizing the person or investigation of any case is figure print. Identifying speculate depend on fingerprint is a proceeding that is exceedingly important to the forensics & law for enforcement agencies. A small numbers of minutiae & the noise attribute make it exceedingly difficult to instinctive match the fingerprints to their acquaintance full prints that are accumulated in databases.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Sustainable Materials (IVCSM-2k20).
Keywords: Correlation | Finger prints | Histogram | Ridges | Segmentation
Ann trained and WOA optimized feature-level fusion of iris and fingerprint
بهینه سازی شبکههای عصبی مصنوعی و WOA آموزش دیده همجوشی در سطح ویژگی عنبیه و اثر انگشت-2021
‘‘Uni Uni-modal Biometric systems has been widely implemented for maintaining security and privacy in various applications like mobile phones, banking apps, airport access control, laptop login etc. Due to Advancement in technologies, imposters designed various ways to breach the security and most of the designed biometric applications security can be compromised. The quality of input sample also play an important role to attain the best performance in terms of improved accuracy and reduced FAR & FRR. Researchers has combined the various biometrics modalities to overcome the problems of Uni-modal bio- metrics. In this paper, a multi biometric feature level fusion system of Iris, and Fingerprint is presented. Due to consistency feature of fingerprint and stability feature of iris modality taken into consideration for high security applications. At pre-processing level, the atmospheric light adjustment algorithm is applied to improve the quality of input samples (Iris and Fingerprint). For feature extraction, the nearest neighbor algorithm and speedup robust feature (SURF) is applied to fingerprint and Iris data respectively. Further, for selecting the best features, the extracted features are optimized by GA algorithm. To achieve an excellent recognition rate, the iris and fingerprint data is trained by ANN algorithm. The experimental results show that the proposed system exhibits the improved performance and better security. Finally, the template is secured by applying the AES algorithm and results are compared with DES, 3DES, RSA and RC4 algorithm.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Con- ference on Computations in Materials and Applied Engineering – 2021.
Keywords: Multimodal biometrics fusion | ANN | SURF | GA | RSA
Finger vein pattern recognition using image processing technique
تشخیص الگوی رگ انگشت با استفاده از تکنیک پردازش تصویر-2021
Human identification based on finger vein pattern is an interesting branch of biometric recognition that is getting attention among the researchers in the recent decade as vein patterns are unique such as iris recognition, face recognition, finger print for authentication and security purposes. The vein pattern uti- lized in this authentication technology refers to the image of vessels within the body which will be seen as a random mesh at the surface of the body. Vein pattern authentication can be applied to almost all people. In the proposed system we have used simple and easy algorithms for processing. The processing steps include image enhancement, thresholding and thinning. The processed images are matched with the images in the database. Thus the proposed biometric system will be effectively used in authentication and security purposes.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 12th National Conference on Recent Advancements in Biomedical Engineering.
Keywords: Fingerprint | Vein recognition | Pattern matching
A survey: Intelligent system for imposter detection
یک مرور: سیستم هوشمند برای تشخیص جعل کننده-2021
This study aims the impostor is a very cunning person who reaches an obsessive stage to perfection in impersonating someone in actual life, concentrates on his biometric. He analyzes the controls, restrictions, and obstacles that he will face to overcome them. The technologies biometric recognition performs a greatly important role in impostor detection. Biometrics properties refer to the automatic recognition of persons depending on their behavioral and physiological characteristics. Biometrics comprises face recognition, fingerprint, voice recognition, retinal scanning, and so on. Biometrics may increment the reliability of an ID card system. In this paper, a review of the concepts mentioned above will be provided. At first, a presentation about a procedural overview of biometric recognition technologies, ID card systems. Then dissection will be presented for the review of the most recent techniques. A description of each concept will be given and a comparison study is achieved with formal discussion and analysis for each approach result introduces in this study. Finally, a summary of the research results is given.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Face recognition | Voice recognition | Finger print | Biometric systems | ID card | Person identification | Impostor detection | Machine learning | Deep neural networks
Design of a fingerprint template protection scheme using elliptical structures
طراحی طرح محافظت از اثر انگشت با استفاده از ساختارهای بیضوی-2021
Although biometric authentication is viewed as more prominent than password or token-based methodology in identity verification, biometric templates are vulnerable to attacks. This paper proposes a new approach for securing fingerprint templates using elliptical structures generated from the fingerprint minutiae. Authors generate a feature vector from the ellipse and will be projected onto a 3D-space to compute a binary string. The resultant binary string is transformed to frequency domain (DFT) and multiplied with a user specific random matrix to make it permanently non-invertible and secure. The results show the efficacy of the proposed method for protecting the fingerprints. c 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Fingerprint | Ellipse | Discrete Fourier transform | Template protection
Alignment-free cancelable fingerprint templates with dual protection
الگوهای اثر انگشت قابل انعطاف بدون تراز با محافظت دوگانه-2021
Cancelable ﬁngerprint templates Discrete wavelet transform Attacks via record multiplicity Cancelable biometrics is an important biometric template protection technique. However, many existing cancelable ﬁngerprint templates suffer post-transformation performance deterioration and the attacks via record multiplicity (ARM). In this paper, we design alignment-free cancelable ﬁngerprint templates with dual protection, which is composed of the window-shift-XOR model and the partial discrete wavelet transform. The former defuses the ARM threat and is combined with the latter to provide dual protection and enhance matching performance. The designed cancelable templates meet the requirements of non-invertibility, diversity and revocability and demonstrate superior recognition accuracy, when evaluated over public databases; for example, the Equal Error Rate of the proposed method in the lost-key scenario under the 1vs1 protocol is 0% for both FVC2002 DB1 and DB2, 1.63% for FVC2002 DB3, 7.35% for FVC2004 DB1 and 4.69% for FVC2004DB2.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Cancelable biometrics | Alignment-free | Cancelable fingerprint templates | Discrete wavelet transform | Attacks via record multiplicity