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Journal articles on the topic 'Personal Authentication'

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Author: Grafiati

Published: 11 September 2023

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1

Noh, Seungil, Jaehan Kim, Seokmin Lee, Youngshin Kang, Cheolsoo Park, and Youngjoo Shin. "Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes." Security and Communication Networks 2022 (September 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/7997509.

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Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.

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2

Ariyama, Hirotaka. "A Personal Authentication Method." IEEJ Transactions on Electronics, Information and Systems 107, no. 1 (1987): 62–66. http://dx.doi.org/10.1541/ieejeiss1987.107.1_62.

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3

Kumar, Ajay, David C. M. Wong, Helen C. Shen, and Anil K. Jain. "Personal authentication using hand images." Pattern Recognition Letters 27, no. 13 (October 2006): 1478–86. http://dx.doi.org/10.1016/j.patrec.2006.02.021.

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4

Ortega-Garcia, J., J. Bigun, D. Reynolds, and J. Gonzalez-Rodriguez. "Authentication gets personal with biometrics." IEEE Signal Processing Magazine 21, no. 2 (March 2004): 50–62. http://dx.doi.org/10.1109/msp.2004.1276113.

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5

Mahesh, Renuka. "Personal Authentication Using Finger Images." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 1 (2015): 389–93. http://dx.doi.org/10.17762/ijritcc2321-8169.150177.

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6

Kang, Hyeok, Keun-Ho Lee, and Gui-Jung Kim. "Safe and convenient personal authentication method using Moiré 3D authentication based on biometric authentication." Cluster Computing 22, S1 (November 22, 2017): 2017–26. http://dx.doi.org/10.1007/s10586-017-1234-4.

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7

Maslova, N. O., and D. O. Polunina. "BIOMETRIC AUTHENTICATION METHODS FOR PERSONAL IDENTIFICATION." Naukovyi visnyk Donetskoho natsionalnoho tekhnichnoho universytetu 1(2), no. 2(3) (2019): 12–20. http://dx.doi.org/10.31474/2415-7902-2019-1(2)-2(3)-12-20.

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8

Kumar, Ajay, and Ch Ravikanth. "Personal Authentication Using Finger Knuckle Surface." IEEE Transactions on Information Forensics and Security 4, no. 1 (March 2009): 98–110. http://dx.doi.org/10.1109/tifs.2008.2011089.

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9

Moon, Jaegeun, and Im Y. Jung. "Authentication for Wireless Personal Area Network." Advanced Science Letters 23, no. 10 (October 1, 2017): 9713–17. http://dx.doi.org/10.1166/asl.2017.9781.

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10

Lin, Hung-Yu, and Lein Harn. "Authentication protocols for personal communication systems." ACM SIGCOMM Computer Communication Review 25, no. 4 (October 1995): 256–61. http://dx.doi.org/10.1145/217391.217456.

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11

Han, Chin-Chuan, Hsu-Liang Cheng, Chih-Lung Lin, and Kuo-Chin Fan. "Personal authentication using palm-print features." Pattern Recognition 36, no. 2 (February 2003): 371–81. http://dx.doi.org/10.1016/s0031-3203(02)00037-7.

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12

Kumar, Ajay, and David Zhang. "Personal authentication using multiple palmprint representation." Pattern Recognition 38, no. 10 (October 2005): 1695–704. http://dx.doi.org/10.1016/j.patcog.2005.03.012.

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13

Fons, Mariano, Francisco Fons, Enrique Cantó, and Mariano López. "FPGA-based Personal Authentication Using Fingerprints." Journal of Signal Processing Systems 66, no. 2 (October 15, 2011): 153–89. http://dx.doi.org/10.1007/s11265-011-0629-3.

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14

Mariño, C., M. G. Penedo, M. Penas, M. J. Carreira, and F. Gonzalez. "Personal authentication using digital retinal images." Pattern Analysis and Applications 9, no. 1 (January 10, 2006): 21–33. http://dx.doi.org/10.1007/s10044-005-0022-6.

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15

Feng, Xuanang, Yi Zuo, Eisuke Kita, and Fumiya Saito. "Personal Authentication Using a Kinect Sensor." Review of Socionetwork Strategies 11, no. 2 (November 28, 2017): 201–15. http://dx.doi.org/10.1007/s12626-017-0010-5.

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16

Zukarnain, Zuriati Ahmad, Amgad Muneer, and Mohd Khairulanuar Ab Aziz. "Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges." Symmetry 14, no. 4 (April 14, 2022): 821. http://dx.doi.org/10.3390/sym14040821.

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Smartphone devices have become an essential part of our daily activities for performing various essential applications containing very confidential information. For this reason, the security of the device and the transactions is required to ensure that the transactions are performed legally. Most regular mobile users’ authentication methods used are passwords and short messages. However, numerous security vulnerabilities are inherent in various authentication schemes. Fingerprint identification and face recognition technology sparked a massive wave of adoption a few years back. The international mobile equipment identity (IMEI) and identity-based public key cryptography (ID-based PKC) have also become widely used options. More complex methods have been introduced, such as the management flow that combines transaction key creation, encryption, and decryption in processing users’ personal information and biometric features. There is also a combination of multiple user-based authentications, such as user’s trip routes initialization with the coordinates of home and office to set template trajectories and stay points for authentication. Therefore, this research aimed to identify the issues with the available authentication methods and the best authentication solution while overcoming the challenges.

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17

B S, Shruthi, and Manasa K B. "USER AUTHENTICATION USING EYE-BLINK PASSWORD." International Journal of Computer Science and Mobile Computing 12, no. 4 (April 30, 2023): 42–46. http://dx.doi.org/10.47760/ijcsmc.2023.v12i04.004.

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Personal identification numbers (PINS) are used to authenticate a user for security purpose. In order to authenticate himself the user should physically input the PIN, which might be susceptible to password cracking via shoulder surfing or thermal tracking. Authenticating a PIN with hands-off eye blinks PIN entry techniques, on the opposite hand, offers a safer password entry option by not leaving any physical footprints behind. User authentication using eye-blink password refers to finding the attention blinks in sequential image frames, and generation of PIN. This paper presents an application with three layer authentication system that are face detection, PIN entry by eye blink and OTP (One Time Password) to avoid thermal tracking attacks and shoulder surfing.

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18

Shin, Siho, Mingu Kang, Jaehyo Jung, and Youn Tae Kim. "Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication." Electronics 10, no. 8 (April 13, 2021): 923. http://dx.doi.org/10.3390/electronics10080923.

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Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

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19

Loginovskiy, O. V., M. E. Koval, and A. A. Shinkarev. "Using the ideal point method to search the best authentication method in corporate information systems." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 22, no. 3 (2022): 5–18. http://dx.doi.org/10.14529/ctcr220301.

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Nowadays, various information systems, including enterprise ones, are becoming increasingly popular. Many of these systems store sensitive data of their users. Basically, this data is protected only by a login and a password, which today can no longer provide a high level of security and guarantee the safety of the data. Along with the development of information systems, methods and tools that attackers can use to get hold of confidential information are also evolving. It is not uncommon to hear news that some of the large companies have leaked its users' personal data. So, in order to minimize the risk of compromising user data, it is worth taking a more careful approach to selecting a method of authenticating users in the system. Aim. To determine the most appropriate method of authentication in enterprise information systems with the help of a mathematical approach and taking into account certain criteria. Materials and methods. The following types of authentication were considered: reusable password authentication, TOTP (Time-based one-time password authentication), SMS-based authentication, biometric authentication, OpenID, SAML (Security Assertion Markup Language). The Pareto set method and the ideal point method were used to determine the most preferable authentication method to implement. Results. In the article, the authors describe the authentication methods considered, the algorithm of their work, and diagrams of their interaction. Using the ideal point method, SAML was determined to be the most appropriate authentication method.

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20

Lee, Sun-Woo, Duk-Kyun Woo, and Pyeong-Soo Mah. "Personal Authentication Method using Segment PPG signals." Journal of the Institute of Electronics and Information Engineers 56, no. 6 (June 30, 2019): 45–51. http://dx.doi.org/10.5573/ieie.2019.56.6.45.

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21

Malassiotis, S., N. Aifanti, and M. G. Strintzis. "Personal Authentication Using 3-D Finger Geometry." IEEE Transactions on Information Forensics and Security 1, no. 1 (March 2006): 12–21. http://dx.doi.org/10.1109/tifs.2005.863508.

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22

Tao, Qian, and Raymond Veldhuis. "Biometric Authentication System on Mobile Personal Devices." IEEE Transactions on Instrumentation and Measurement 59, no. 4 (April 2010): 763–73. http://dx.doi.org/10.1109/tim.2009.2037873.

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23

Lim, Dai-Hwan. "Personal Authentication System Using Multimodal Biometric Algorithm." Journal of Korean Institute of Information Technology 15, no. 12 (December 21, 2017): 147–56. http://dx.doi.org/10.14801/jkiit.2017.15.12.147.

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24

Hsu, Chih-Bin. "Personal authentication through dorsal hand vein patterns." Optical Engineering 50, no. 8 (August 1, 2011): 087201. http://dx.doi.org/10.1117/1.3607413.

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25

Zhang, Chongyang. "Representation of face images for personal authentication." Optik - International Journal for Light and Electron Optics 124, no. 17 (September 2013): 2985–92. http://dx.doi.org/10.1016/j.ijleo.2012.09.015.

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26

Umamaheswari.R, Umamaheswari R. "An Enhanced Biometric System for Personal Authentication." IOSR Journal of Electronics and Communication Engineering 6, no. 3 (2013): 63–69. http://dx.doi.org/10.9790/2834-0636396.

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27

Vyshnevsky, V. V., T. M. Romanenko, Yu O. Luhovsky, and O. F. Boretsky. "Data preparation services for the authentication of people by their electrocardiogram." Mathematical machines and systems 3 (2022): 58–69. http://dx.doi.org/10.34121/1028-9763-2022-3-58-69.

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For modern telemedicine, it is important to solve the problem of patient authentication. This paper studies the possibility of using personal unique features of people, namely their electro-cardiogram (ECG), for authentication. Biometric authentication is promising in telemedicine, where patients send physicians through information channels increasing amounts of their data. When some clinic monitors its patient remotely and asks them for an ECG, it should be confi-dent that the received ECG belongs to that particular patient. Biometric authentication using ECG is a rather specific area of research and now there are no leading technologies in this field. This method is the goal of the technology being developed. In this direction, we have already conducted research, developed algorithms, and tested their viability on test data. Combining the obtained algorithms and machine learning to solve the problem of classification of ECG belong-ing to a person, there was developed a program capable of authenticating people by their ECG. This gave confidence in the possibility of using such technology. The next step was to scale this technology to reach two goals: to test the performance of the technology on a much larger amount of data and to prepare it for use in real conditions when processing time should be min-imal. The paper describes the gradual development from a program running on a personal com-puter to a system performing computing in a cloud environment. The time spent on conducting experiments and comparing the running time of programs on a personal computer and in a cloud environment are shown. The most efficient configuration of the system for cloud architecture was found experimentally.

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28

Huang, Juanjuan, Yuhang Xue, and Linhui Liu. "Dynamic Signature Verification Technique for the Online and Offline Representation of Electronic Signatures in Biometric Systems." Processes 11, no. 1 (January 6, 2023): 190. http://dx.doi.org/10.3390/pr11010190.

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Biometric systems input physical or personal human characteristics for identification, authentication, and security purposes. With the advancement in communication and intelligent security systems, biometrics are programmed to validate electronic signatures (E-signatures) for online and offline authentication. This article introduces a dynamic signature verification technique (DSVT) using mutual compliance (MC) between the security system and the biometric device. The security system is responsible for online and offline signature approval using personal inputs from humans. This personal verification is related to the stored online/offline signatures using certificates provided for authentication. The certificate-based authentication is valid within a session for online representation. Contrarily, this authentication is valid for persons under offline conditions. In this mode of segregation, application-level authentication verification is performed. A conventional tree classifier for dynamic signature verification is used for differentiating online and offline signatures. Moreover, the security metrics—such as signing bit, key, and size—are verified for both modes using classifier learning. For the segregated mode, the validation of the above is required to be unanimous to accelerate the dynamicity. The proposed technique’s performance is analyzed using the authentication success rate, verification failing ratio, verification time, and complexity.

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29

R, Dr Sharavanakumar. "EYE Blinking for Password Authentication." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2758–66. http://dx.doi.org/10.22214/ijraset.2022.44434.

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Abstract: Personal identification numbers area unit used for user authentication and security. word verification exploitation PINs requires users to enter a physical PIN, which may be vulnerable to word breakage or hacking via shoulder water sport or thermal chase. PIN authentication with eye blinks entry techniques, doesn't leave any quite physical footprints behind and therefore provide a safer word entry possibility. This project presents a time period application to avoid. The personal identification numbers (PINS) is a common user authentication method for many applications, such as money transaction is online banking application and automatic teller machine(ATMs).unlocking personal devices ,event centers, shopping malls, Medical centers, schools/collages and opening Doors. I

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30

Senthil Kumar, T., S. Prabhakaran, and V. Prashanth. "Four-way handshake protocol for authenticating in multiple mix-zones." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 200. http://dx.doi.org/10.14419/ijet.v7i2.4.13039.

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Authentication is the process of verifying that the users who they claim to be or not, it is based on identity and credentials. Most of the attacks can be reduced using authentication process. Authentication is important because as the amount of online data sharing has increased, threats and fraud in a large amount are also increased, a changing of the guard which provides security to mobile devices is needed for which authentication is necessary. Privacy of user’s location is important in mobile networks, there are several strategies to protect the personal information (i.e., their location). In previous work it is introduced that the mix zone model which will change the old pseudonyms to new pseudonyms and anonymizes user’s identity by restricting the position where users can be located. Later work, even in the multiple mix-zones model, attackers can attack by using side information (like footprints, navigation etc.). So, we need an authentication protocol while two mix-zones or user-services are communicating. We came across different authentication protocols like PAP, CHAP, and EAP. In this paper, a four-way handshake protocol is implemented for providing authentication while multiple mix-zones are communicating. A four-way handshake authentication protocol i.e., WPA-PSK protocol for verification. WPA-PSK is applied in such a way that both STA(supplicant) and AP(authenticator) can check that they are re-agreeing on a non-forged RSN and IE, therefore they are using the most secure available protocols.

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Nanglae, Nilobon, Bello Musa Yakubu, and Pattarasinee Bhattarakosol. "Extraction of Hidden Authentication Factors from Possessive Information." Journal of Sensor and Actuator Networks 12, no. 4 (August 11, 2023): 62. http://dx.doi.org/10.3390/jsan12040062.

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Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution.

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32

Rajagopal, Gayathri, and Senthil Kumar Manoharan. "Personal Authentication Using Multifeatures Multispectral Palm Print Traits." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/861629.

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Biometrics authentication is an effective method for automatically recognizing a person’s identity with high confidence. Multispectral palm print biometric system is relatively new biometric technology and is in the progression of being endlessly refined and developed. Multispectral palm print biometric system is a promising biometric technology for use in various applications including banking solutions, access control, hospital, construction, and forensic applications. This paper proposes a multispectral palm print recognition method with extraction of multiple features using kernel principal component analysis and modified finite radon transform. Finally, the images are classified using Local MeanK-Nearest Centroid Neighbor algorithm. The proposed method efficiently accommodates the rotational, potential deformations and translational changes by encoding the orientation conserving features. The proposed system analyses the hand vascular authentication using two databases acquired with touch-based and contactless imaging setup collected from multispectral Poly U palm print database and CASIA database. The experimental results clearly demonstrate that the proposed multispectral palm print authentication obtained better result compared to other methods discussed in the literature.

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33

Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Smartphone Authentication System Using Personal Gaits and a Deep Learning Model." Sensors 23, no. 14 (July 14, 2023): 6395. http://dx.doi.org/10.3390/s23146395.

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In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication.

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34

Rajasingh, J. Paul, and D. Sai Yaswanth. "Fingerprint Authentication." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 87–89. http://dx.doi.org/10.35940/ijeat.e2651.0610521.

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Biometrics refers to the automatic identification of a living person based on physiological or behavioural characteristics for authentication purpose. Among the existing biometric technologies are the face recognisation, fingerprint recognition, finger-geometry, hand geometry, iris recognition, vein recognition, voice recognition and signature recognition, Biometric method requires the physical presence of the person to be identified. This emphasizes its preference over the traditional method of identifying what you have such as, the use of password, a smartcard etc. Also, it potentially prevents unauthorized admittance to access control systems or fraudulent use of ATMs, Time Attendance Systems, cellular phones, smart cards, desktop PCs, Workstations, vehicles and computer networks. Biometric recognition systems offer greater security and convenience than traditional methods of personal recognition.

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35

Chaudhary, Sumit, Monika Garg, and Neha Singh. "IMPLEMENTATION OF FINGER TOKEN AUTHENTICATION TECHNIQUE USING ARTIFICIAL INTELLIGENCE APPROACH IN NTT MICROSYSTEMS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (November 30, 2012): 428–32. http://dx.doi.org/10.24297/ijct.v3i3a.2950.

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Personal authentication that correctly identifies valid users in an indispensable security technology for enabling people to enjoy the access to data services with a sense of confidence and assurance. Passwords have been extensively used to identify authorized users in the past, but passwords alone are vulnerable because people tend to choose weak password relating to some aspect of them (e.g. birthday, phone number, family name) that can be guessed. This has led to growing interest in biometric authentication using fingerprints that make it possible to identify personal authentication correctly. It eliminates problems of forgotten passwords or lost cards and is currently becoming more popular for convenient and secure authentication. NTT Microsystems is a new and exclusive technology that overcomes the limitations of previous systems and sets a new standard for compact, reliable and low-cost fingerprint authentication. NTT laboratories have developed Finger Token, a portable fingerprint identification device that substitutes a fingerprint instead of password for authentication

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Kim, Hanvit, Haena Kim, Se Chun, Jae-Hwan Kang, Ian Oakley, Youryang Lee, Jun Ryu, et al. "A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors." Sensors 18, no. 8 (August 20, 2018): 2738. http://dx.doi.org/10.3390/s18082738.

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Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

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Asha G. M, Asha G. M., and Latha B. M. Latha B. M. "Feature Extraction of an Iris for Personal Authentication Based on Gabor Wavelet." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 260–62. http://dx.doi.org/10.15373/22778179/may2013/86.

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38

Yang, Tay Yi, Palaniappan Shamala, Muruga Chinniah, and Cik Feresa Mohd Foozy. "Graphical Password Authentication For Child Personal Storage Application." Journal of Physics: Conference Series 1793, no. 1 (February 1, 2021): 012065. http://dx.doi.org/10.1088/1742-6596/1793/1/012065.

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39

ZHU, Le-Qing, San-Yuan ZHANG, and Rui XING. "Automatic Personal Authentication Based on Finger Phalangeal Prints." Acta Automatica Sinica 35, no. 7 (August 10, 2009): 875–81. http://dx.doi.org/10.3724/sp.j.1004.2009.00875.

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40

Thapliyal, Amitabh, Om Prakash Verma, and Amioy Kumar. "Behavioral biometric based personal authentication in feature phones." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 802. http://dx.doi.org/10.11591/ijece.v12i1.pp802-815.

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<p><span>The usage of mobile phones has increased multifold in the recent decades mostly because of its utility in most of the aspects of daily life, such as communications, entertainment, and financial transactions. Feature phones are generally the keyboard based or lower version of touch based mobile phones, mostly targeted for efficient calling and messaging. In comparison to smart phones, feature phones have no provision of a biometrics system for the user access. The literature, have shown very less attempts in designing a biometrics system which could be most suitable to the low-cost feature phones. A biometric system utilizes the features and attributes based on the physiological or behavioral properties of the individual. In this research, we explore the usefulness of keystroke dynamics for feature phones which offers an efficient and versatile biometric framework. In our research, we have suggested an approach to incorporate the user’s typing patterns to enhance the security in the feature phone. We have applied k-nearest neighbors (k-NN) with fuzzy logic and achieved the equal error rate (EER) 1.88% to get the better accuracy. The experiments are performed with 25 users on Samsung On7 Pro C3590. On comparison, our proposed technique is competitive with almost all the other techniques available in the literature.</span></p>

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41

Verma, Gaurav, and Aloka Sinha. "Digital holographic-based cancellable biometric for personal authentication." Journal of Optics 18, no. 5 (March 24, 2016): 055705. http://dx.doi.org/10.1088/2040-8978/18/5/055705.

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Xiangqian Wu, D. Zhang, and Kuanquan Wang. "Palm line extraction and matching for personal authentication." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 36, no. 5 (September 2006): 978–87. http://dx.doi.org/10.1109/tsmca.2006.871797.

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Hsu, Chih-Bin. "Errata: Personal authentication through dorsal hand vein patterns." Optical Engineering 50, no. 9 (September 1, 2011): 099802. http://dx.doi.org/10.1117/1.3645079.

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Zhang, Lin, Lei Zhang, David Zhang, and Hailong Zhu. "Online finger-knuckle-print verification for personal authentication." Pattern Recognition 43, no. 7 (July 2010): 2560–71. http://dx.doi.org/10.1016/j.patcog.2010.01.020.

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Kang, Jae-Hwan, Young Chang Jo, and Sung-Phil Kim. "Electroencephalographic feature evaluation for improving personal authentication performance." Neurocomputing 287 (April 2018): 93–101. http://dx.doi.org/10.1016/j.neucom.2018.01.074.

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Karnan, M., M. Akila, and N. Krishnaraj. "Biometric personal authentication using keystroke dynamics: A review." Applied Soft Computing 11, no. 2 (March 2011): 1565–73. http://dx.doi.org/10.1016/j.asoc.2010.08.003.

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Saito, F., A. Hara, N. Kubo, and E. Kita. "1406 Study on Personal Authentication by Kinect Sensor." Proceedings of Design & Systems Conference 2014.24 (2014): _1406–1_—_1406–5_. http://dx.doi.org/10.1299/jsmedsd.2014.24._1406-1_.

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Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Smartphone Authentication System using Personal Gaits and CNN." Journal of Korean Institute of Information Technology 20, no. 10 (October 31, 2022): 71–82. http://dx.doi.org/10.14801/jkiit.2022.20.10.71.

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Ziyatbekova, G. Z., and M. A. Shaikhy. "Biometric technology for personal identification and authentication: fingerprint." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 86, no. 4 (December 30, 2022): 62–70. http://dx.doi.org/10.47533/2020.1606-146x.196.

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Abstract:

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

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Bao, Song De. "A Security Authentication Method of Mobile RFID." Advanced Materials Research 268-270 (July 2011): 1077–81. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1077.

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Abstract:

As the internet technology develops, the application of mobile RFID becomes more and more common. Since mobile RFID label is easier to be read and attacked by hacker, personal information of the users is leaked. Present authentication mechanisms in application are either made into RFID hardware with complex design or with weaker security protection ability. The paper proposes a new security authentication mechanism and the test result shows that the authentication mechanism may effectively prevent various attack behaviors of hack with the base of low cost so as to reach the objective of protecting personal privacy of card holder.

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