A survey on Face Recognition Technique

Sithara, Ramesh; Rajasree, R.

doi:10.1109/icci46240.2019.9404387

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The extracted features include facial contours, facial features (i.e., eyes, nose, mouth, eyebrows, etc. ), texture features (e.g., wrinkles) [35]. Wrinkle texture features affect the detection of age and health.…”

Section: Face Feature Extractionmentioning

confidence: 99%

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Liu¹,

Yu²

2023

J Multimed Inf Syst

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Although the current face de-recognition algorithm can effectively protect the user's identity, it will change the original attributes of the image, destroy the visual effect, or change the characteristics of the image to varying degrees. Traditional methods such as pixelation and occlusion can effectively realize face de-recognition, but the processed image cannot be used for basic processing such as face statistics and expression recognition, which greatly destroys the usability of the image in the field of online new media. In order to preserve more attributes of the original image and reduce the changes of facial skin color and texture features caused by the above de-recognition algorithm's operation on the whole facial area, this paper proposes a privacy protection model of face image based on generative adversarial network and facial feature analysis. Our model realizes face recognition based on changing the original image as little as possible. Specifically, we identify and extract features from fixed parts in face images, including eyes, nose, mouth, eyebrows, and cheeks. Then, we designed a face privacy recognition and protection model based on conditional generative adversarial networks. We verify our proposed method on two most commonly used face datasets in face research, namely CelebFaces Attributes and Labeled Faces in the Wild, and the results show that our proposed method achieve better performance compared with other face privacy protection methods.

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Section: Face Feature Extractionmentioning

confidence: 99%

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Liu¹,

Yu²

2023

J Multimed Inf Syst

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“…Face detection, face alignment, face feature extraction, and feature matching with the stored template are the steps involved in face recognition (Sithara & Rajasree, 2019;Alsrehin & Al-Taamneh, 2020;Sahu & Dash, 2020;Malakar et al, 2021;Badave & Kuber, 2021;Preethi & Vodithala, 2021). Likewise, face recognition systems can be image-based, video sequence-based, and sensory data-based matching .…”

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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“…The face recognition systems use artificial intelligence and other specialised technologies to identify and verify human faces (Yu et al, 2018). Face detection, face alignment, face feature extraction, and feature matching with the template are the steps involved in face recognition (Sithara & Rajasree, 2019;Alsrehin & Al-Taamneh, 2020;Sahu & Dash, 2020;Malakar et al, 2021;Badave & Kuber, 2021;Preethi & Vodithala, 2021). Likewise, face recognition systems can be image-based, video sequencebased, and sensory data-based matching .…”

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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A survey on Face Recognition Technique

Cited by 5 publications

References 7 publications

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

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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