Access Control System Based on Face Recognition

Shavetov, Sergei; Sivtsov, Vladimir

doi:10.1109/codit49905.2020.9263894

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…This shows that the facial recognition system can be used for various purposes and can continue to be developed. Shavetov and Sivtsov [20] researched facial recognition for an access control system using Viola-Jones and MultiTask CNN (MTCNN). The results of this study were able to match faces from online cameras to images in the database.…”

Section: Introductionmentioning

confidence: 99%

Room Security System Using Machine Learning with Face Recognition Verification

Sunardi,

Fadlil,

Prayogi

2023

RIA

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Machine Learning (ML), an intelligent system known for its capacity to automate procedures by discerning patterns pertinent to specific tasks such as detection, prediction, and pattern recognition, is increasingly being used to advance biometric technologies. Among these, facial recognition, a subset of computer vision-based biometrics, is emerging as a robust security measure. The present study is centered on the design of a room security system that leverages facial recognition, rooted in a Convolutional Neural Network (CNN) architecture. The CNN model was constructed within the Tensorflow framework, employing the Keras library and Scikit-learn, all embedded within a Raspberry Pi system. The model was trained on 15 registered face classes, with an additional three unregistered classes used for biometric security testing. Performance was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR), metrics that assess the system's ability to accurately verify authorized and unauthorized users. Findings demonstrated that the CNN model achieved a 97% accuracy rate in facial identification. Furthermore, biometric security testing of the CNN model using room security devices yielded optimal results at a threshold of 90%, with FAR=26.67%, FRR=9.33%, and an Equal Error Rate (EER) of 21.33%. It was observed that factors such as lighting, data variation, resolution, and positional changes during data sampling could impact the system's performance in realtime operations. It is therefore recommended that data collection and facial scanning be consistently conducted under identical environmental conditions to enhance the accuracy of the system. This study signifies a substantial stride in the development of advanced room security systems, thus contributing to the broader realm of secure access control systems.

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Section: Introductionmentioning

confidence: 99%

Room Security System Using Machine Learning with Face Recognition Verification

Sunardi,

Fadlil,

Prayogi

2023

RIA

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“…With the advancement in smart cameras and mobile device technologies coupled with the high demand for security and convenience, face recognition has gained acknowledgement in pattern recognition (Guha, 2021;Yang et al, 2021). Face recognition is defined as the process of using a person's facial features like eyes, eyebrows, nose, mouth, cheeks, skin colour, lips, chin, ears, face shape, beards, wrinkles, and the distance between them to identify users by comparing these features with the digital image or a video frame stored in the database (Alsrehin & Al-Taamneh, 2020;Lin & Xie, 2020;Shavetov & Sivtsov, 2020;Bai et al, 2020;Badave & Kuber, 2021). During face recognition, a person's facial image is obtained using the cameras and videos from security and surveillance systems and applied to various pattern recognition algorithms to get unique features used for identification (Thomas, 2021;Yang et al, 2021;Preethi & Vodithala, 2021).…”

Section: (Ii) Face Recognitionmentioning

confidence: 99%

“…Face recognition is mainly adopted in applications because of its uniqueness, security, and accuracy, and it requires a camera for verification that is easy to install and use (Abbas et al, 2017;Shavetov & Sivtsov, 2020;Thomas, 2021). Much as face recognition is implemented in many applications, they encounter many challenges such as similar faces, face deformation, age factor, illumination changes, direction, occlusion, pose and expression changes, shading, and dynamic background (Li, 2019;Sahu & Dash, 2020;Luo et al, 2021;Guha, 2021;Badave & Kuber, 2021).…”

Section: (Ii) Face Recognitionmentioning

confidence: 99%

Development of a secure multi-factor authentication algorithm for mobile money applications

Guma¹

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With the evolution of industry 4.0, financial technologies have become paramount and mobile money as one of the financial technologies has immensely contributed to improving financial inclusion among the unbanked population. Several mobile money schemes were developed but, they suffered severe authentication security challenges since they implemented two-factor authentication. This study focused on developing a secure multi-factor authentication (MFA) algorithm for mobile money applications. It uses personal identification numbers, one-time passwords, biometric fingerprints, and quick response codes to authenticate and authorize mobile money subscribers. Secure hash algorithm-256, Rivest-Shamir-Adleman encryption, and Fernet encryption were used to secure the authentication factors, confidential financial information and data before transmission to the remote databases. A literature review, survey, evolutionary prototyping model, and heuristic evaluation and usability testing methods were used to identify authentication issues, develop prototypes of native genuine mobile money (G-MoMo) applications, and identify usability issues with the interface designs and ascertain their usability, respectively. The results of the review grouped the threat models into attacks against privacy, authentication, confidentiality, integrity, and availability. The survey identified authentication attacks, identity theft, phishing attacks, and PIN sharing as the key mobile money systems’ security issues. The researcher designed a secure MFA algorithm for mobile money applications and developed three native G-MoMo applications to implement the designed algorithm to prove the feasibility of the algorithm and that it provided robust security. The algorithm was resilient to non-repudiation, ensured strong authentication security, data confidentiality, integrity, privacy, and user anonymity, was highly effective against several attacks but had high communication overhead and computational costs. Nevertheless, the heuristic evaluation results showed that the G-MoMo applications’ interface designs lacked forward navigation buttons, uniformity in the applications’ menu titles, search fields, actions needed for recovery, and help and documentation. Similarly, the usability testing revealed that they were easy to learn, effective, efficient, memorable, with few errors, subscriber satisfaction, easy to use, aesthetic, easy to integrate, and understandable. Implementing a secure mobile money authentication and authorisation by combining multiple factors which are securely stored helps mobile money subscribers and other stakeholders to have trust in the developed native G-MoMo applications.

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“…With the advancement in smart cameras and mobile device technologies coupled with the high demand for security and convenience, face recognition has gained recognition in pattern recognition (Guha, 2021;Yang et al, 2021). Face recognition is defined as the process of using a person's facial features like eyes, eyebrows, nose, mouth, cheeks, skin colour, lips, chin, ears, face shape, beards, wrinkles, and the distance between them to identify users by comparing these features with the digital image or a video frame stored in the database (Alsrehin & Al-Taamneh, 2020; Lin & Xie, 2020;Shavetov & Sivtsov, 2020;Bai et al, 2020;Badave & Kuber, 2021). During face recognition, a person's facial image is obtained using the cameras and videos from security and surveillance systems and applied to various pattern recognition algorithms to get unique features used for identification (Thomas, 2021;Yang et al, 2021;Preethi & Vodithala, 2021).…”

Section: (B) Face Recognitionmentioning

confidence: 99%

Development of a secure multi-factor authentication algorithm for mobile money applications

Ali

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With the expansion of industry 4.0, financial technology (FinTech) has become paramount in this era. Mobile money as one of the FinTech has immensely contributed to improving financial inclusions among the unbanked population in many developing countries. Several mobile money schemes were developed to ensure easy access to mobile money services. However, they have suffered severe authentication security challenges since implementing two-factor authentication (2FA). Therefore, this research developed a secure multi-factor authentication (MFA) algorithm for mobile money applications that combines personal identification number (PIN), one-time password (OTP), and biometric fingerprints to authenticate the mobile money subscribers. It also used the customer’s biometric fingerprints and the agent’s quick response (QR) code to authorise money withdrawal. The PINs and OTP are secured by secure hashing algorithm-256 (SHA-256) and biometric fingerprints by Fast IDentity Online (FIDO), where the Rivest-Shamir-Adleman (RSA) encryption protects the public/private key pair and the fingerprint templates. The QR codes, confidential financial information in the databases, and all the data before transmission to the remote databases are secured using Fernet encryption. A design science research approach was employed in the research using a mixed-method. The review results identified and grouped the threat models into attacks against privacy, authentication, confidentiality, integrity, and availability. The cryptographic functions and personal identification were the countermeasures. The survey identified authentication attacks, identity theft, phishing attacks, and PIN sharing as the crucial security issues Uganda’s mobile money systems encountered. The security analysis of the designed algorithm and developed native genuine mobile money (G-MoMo) applications proved that it provided robust security during authentication and ensured data confidentiality, integrity, privacy and user anonymity. It is highly effective against several security attacks and resilient to non-repudiation. The performance analysis results showed that the algorithm enhanced security but had high communication overhead and computational cost. Lack of a forward navigation button, lack of uniformity in the applications menu title, lack of search field options, lack of actions needed for recovery, and lack of help & documentation, were identified as the results of the usability issues with the native G-MoMo applications’ user interfaces. While the results of the usability testing showed that the native G-MoMo applications were learnable, effective, efficient, memorable, had few errors, satisfaction, ease of use, aesthetic, helpful, easy to integrate, and understandable. In conclusion, implementing a secure mobile money authentication using the ii novel approach combining multiple factors helps mobile money subscribers and other stakeholders trust the mobile money industry since the security goals are highly maintained.

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Access Control System Based on Face Recognition

Cited by 11 publications

References 9 publications

Room Security System Using Machine Learning with Face Recognition Verification

Room Security System Using Machine Learning with Face Recognition Verification

Development of a secure multi-factor authentication algorithm for mobile money applications

Development of a secure multi-factor authentication algorithm for mobile money applications

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