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Journal articles on the topic 'Face authentication'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

R.S., Dr Sabeenian. "Attendance Authentication System Using Face Recognition." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1235–48. http://dx.doi.org/10.5373/jardcs/v12sp4/20201599.

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2

Beumier, C., and M. Acheroy. "Automatic 3D face authentication." Image and Vision Computing 18, no. 4 (March 2000): 315–21. http://dx.doi.org/10.1016/s0262-8856(99)00052-9.

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3

Chen, Xiang, Shouzhi Xu, Kai Ma, and Peng Chen. "Cross-Domain Identity Authentication Protocol of Consortium Blockchain Based on Face Recognition." Information 13, no. 11 (November 10, 2022): 535. http://dx.doi.org/10.3390/info13110535.

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A consortium system can leverage information to improve workflows, accountability, and transparency through setting up a backbone for these cross-company and cross-discipline solutions, which make it become a hot spot of market application. Users of a consortium system may register and log in different target domains to get the access authentications, so how to access resources in different domains efficiently to avoid the trust-island problem is a big challenge. Cross-domain authentication is a kind of technology that breaks trust islands and enables users to access resources and services in different domains with the same credentials, which reduces service costs for all parties. Aiming at the problems of traditional cross-domain authentication, such as complex certificate management, low authentication efficiency, and being unable to prevent the attack users’ accounts, a cross-domain authentication protocol based on face recognition is proposed in this paper. The protocol makes use of the decentralized and distributed characteristics of the consortium chain to ensure the reliable transmission of data between participants without trust relationships, and achieves biometric authentication to further solve the problem of account attack by applying a deep-learning face-recognition model. An asymmetric encryption algorithm is used to encrypt and store the face feature codes on the chain to ensure the privacy of the user’s face features. Finally, through security analysis, it is proved that the proposed protocol can effectively prevent a man-in-the-middle attack, a replay attack, an account attack, an internal attack, and other attacks, and mutual security authentication between different domains can be realized with the protocol.

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4

Reddy, Reddy Phanidhar. "Voice and Face Recognition for Web Browser Security." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 199–205. http://dx.doi.org/10.22214/ijraset.2021.38777.

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Abstract: This paper analysis about browser privacy multimodal authentication mechanisms. Face and voice recognition will be used as authentication methods in this process. The OpenCV library is used in the framework's face recognition section. It detects and recognizes faces from a database using basic eigen face recognition approaches. The MFCC (Mel Frequency Cepstrum Coefficients) and Gaussian Mixture Model are used to recognize voices. Following successful authentication, the cookies on the local hard disc are decrypted, allowing us access to the browser cookies. Initially, after a user registers, we will encrypt the browser cookies with AES, one of the most secure encryption methods available. keywords: MFCC, Gaussian Mixture Model, Browser cookies, authentication, AES, encryption, decryption, Open CV, Eigen.

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Et. al., R. P. Dahake,. "Face Recognition from Video using Threshold based Clustering." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 272–85. http://dx.doi.org/10.17762/turcomat.v12i1s.1768.

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Video processing has gained significant attention due to the rapid growth in video feed collected from a variety of domains. Face recognition and summary generation is gaining attention in the branch of video data processing. The recognition includes face identification from video frames and face authentication. The face authentication is nothing but labelling the faces. Face recognition strategies used in image processing techniques cannot be directly applied to video processing due to bulk data. The video processing techniques face multiple problems such as pose variation, expression variation, illumination variation, camera angles, etc. A lot of research work is done for face authentication in terms of accuracy and efficiency improvement. The second important aspect is the video summarization. Very few works have been done on the video summarization due to its complexity, computational overhead, and lack of appropriate training data. In some of the existing work analysing celebrity video for finding association in name node or face node of video dataset using graphical representation need script or dynamic caption details As well as there can be multiple faces of same person per frame so using K- Means clustering further for recognition purpose needs cluster count initially considering total person in the video. The proposed system works on video face recognition and summary generation. The system automatically identifies the front and profile faces of users. The similar faces are grouped together using threshold based a fixed-width clustering which is one of the novel approach in face recognition process best of our knowledge and only top k faces are used for authentication. This improves system efficiency. After face authentication, the occurrence count of each user is extracted and a visual co-occurrence graph is generated as a video summarization. The system is tested on the video dataset of multi persons occurring in different videos. Total 20 videos are consider for training and testing containing multiple person in one frame. To evaluate the accuracy of recognition. 80% of faces are correctly identified and authenticated from the video.

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Sadashiv, Wagh Kishor, Anushka Bagal, Abhijit Khatri, Maitrayee Dhumal, and Sofiya Shaikh. "Face and Voice Authentication System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1670–73. http://dx.doi.org/10.22214/ijraset.2022.42452.

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Abstract: The issue of design and security is very predominant in any financial and business organization, especially such organization as a bank. Therefore, we intend to aid in security of the bank by bringing in an Artificial intelligence system that involves an individual to get an access to some items using face and voice recognition security system. This AI system is not just a normal password lock system that require a user to insert password and gain access to some items, it is a system that has an administrative authentication. In addition, with this kind of security authentication system we intend to implement, a highly secured AI feature, which enables the user with assured and highly secured transactions using their personal frame. Here an individual have to provide the face and voice authentication, which uses different algorithms, and is read by the AI server for clarification and verification. From this project, we hope to build an alternative and highly verified security for banks. Keywords: Artificial intelligence, administrative authentication, secured transactions, financial, business, organization.

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7

Hanafi, M., and W. A. W. Adnan. "Boosted Features for Face Authentication." Applied Mechanics and Materials 666 (October 2014): 276–81. http://dx.doi.org/10.4028/www.scientific.net/amm.666.276.

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Boosted features developed using face signatures in combination with Gentle Adaboost algorithm offer alternative features for face authentication and face recognition. Face signatures are face representations extracted from Trace transform and Gentle Adaboost is used to enhance the performance of the features extracted from the face signatures. In this paper, we demonstrate the usefulness of the constructed features with experiments on BANCA database.

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8

Premkumar, J., K. Sughasri, Angel Preethi, and E. Kavitha. "Automized Pharmacy Using Face Authentication." Journal of Physics: Conference Series 1937, no. 1 (June 1, 2021): 012024. http://dx.doi.org/10.1088/1742-6596/1937/1/012024.

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9

Guodong Guo, Lingyun Wen, and Shuicheng Yan. "Face Authentication With Makeup Changes." IEEE Transactions on Circuits and Systems for Video Technology 24, no. 5 (May 2014): 814–25. http://dx.doi.org/10.1109/tcsvt.2013.2280076.

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10

Tistarelli, M., and E. Grosso. "Active vision-based face authentication." Image and Vision Computing 18, no. 4 (March 2000): 299–314. http://dx.doi.org/10.1016/s0262-8856(99)00059-1.

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11

Pigeon, Stéphane, and Luc Vandendorpe. "Image-based multimodal face authentication." Signal Processing 69, no. 1 (August 1998): 59–79. http://dx.doi.org/10.1016/s0165-1684(98)00087-5.

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12

KULKARNI, SARVESH. "SMART AUTHENTICATION USING FACE RECOGNITION." International Journal of Recent Advancement in Engineering & Research 3, no. 7 (July 22, 2017): 9. http://dx.doi.org/10.24128/ijraer.2017.gh89jk.

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13

Kang, Sun-Kyoung. "Fast Identity Online-Based Facial Authentication System Development in Mobile." Journal of Computational and Theoretical Nanoscience 18, no. 5 (May 1, 2021): 1582–85. http://dx.doi.org/10.1166/jctn.2021.9599.

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Recently, research on methods to improve upon the security vulnerability of password based authentication is being actively conducted. Research on how to use real-time biometric recognition is also actively underway. The system development of Public Key Infrastructure (PKI) key pairs and uses a method that safely stores the produced keys in secure zones, and encryption key data is composed so that access can only be made in Fast Identity Online (FIDO) Authenticator zones. Face certification data extracted from the face recognition engine is created, data can be accessed and saved through encryption. With the Fast Identity Online (FIDO) Authenticator, a method was developed that compares and face certification data. The system in this thesis developed a Fast Identity Online (FIDO) based face certification system that uses biometric authentication to supplement the weaknesses of password based systems which were used for user certification in the past because they cost little and are convenient. System development in this thesis was conducted by applying a Fast Identity Online (FIDO) based certification system to an Active Shape Model (ASM) style face recognition algorithm. After taking a photo of the face with the user’s cellular phone and saving it, certification is carried out through comparisons made of the photo and real-time input images. Once certification is made using Fast Identity Online (FIDO) protocols, tests of face detection speed and matching speed are conducted to measure accuracy and speed. A total of 30 tests were conducted and test results showed that detection speed was an average of 30.35 f/s and matching speed was 14.16 ms. In this paper, biometric authentication security and convenience are provided by using security functions provided by user devices and supplementing them with a server operation method.

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Chinwe C, Ibebuogu, Philip Seth, and Anyaduba Obiageli J. "Biometric Authentication System Using Face Geometry." International Journal of Engineering and Computer Science 8, no. 08 (August 20, 2019): 24805–13. http://dx.doi.org/10.18535/ijecs/v8i08.4332.

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This seminar paper deals with biometric authentication, using human facial geometry as the login verification parameter. Biometric is the measurement and statistical analysis of human's unique physical and behavioral characteristics by mapping face geometry, fingerprints, iris, and voice. The aim of this seminar paper is to develop a functional Biometric Authentication System Using Face Geometry as the authentication method. Facial Geometry Authentication is a category of biometric technology that maps an individual's facial features mathematically and stores the data as face-prints in a database. Meanwhile the objective of this paper is to improve data access security, enhance identification accuracy, and contribute to the improvement of the existing facial recognition systems, focusing mainly on increasing its accuracy performance. On the other hand, hacking into users’ privacy, loss of confidential information, and high running costs, among others geared the motivation to develop a biometric authentication system using face geometry. Furthermore, the software engineering methodology adopted in this seminar paper is the Structural System Analysis and Design Methodology (SSADM). The software is developed in Visual C-sharp, and the database in SQL Server 2012; using Microsoft Visual Studio 2012 as the integrated development environment. Besides, OpenCV 2.4.8 library is used for image processing, image mapping, and computer vision. The expected result of this seminar paper include: improved user data security, and increased efficiency in facial detection/recognition.

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15

LEE, RAYMOND S. T. "iJADE AUTHENTICATOR — AN INTELLIGENT MULTIAGENT BASED FACIAL AUTHENTICATION SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 04 (June 2002): 481–500. http://dx.doi.org/10.1142/s0218001402001794.

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In modern consumer e-shopping environments, customer authentication is a critical process for confirming the identity of the customer. Traditional authentication techniques that rely on the customers to proactively identify themselves (using various schemes) can affect the user-friendliness of the e-shopping experience, and therefore reduce the customers' preference for such facilities. In this paper, we propose an innovative intelligent multiagent-based environment, called iJADE (intelligent Java Agent Development Environment) to provide an intelligent agent-based platform in the e-commerce environment. Contemporary agent development platforms are focused on the autonomy and mobility of the agents, whereas iJADE provides an intelligent layer (known as the "conscious layer") to implement various AI (artificial intelligence) functionalities in order to produce "smart" agents. From an implementation perspective, we introduce an innovative e-shopping authentication scheme called the "iJADE Authenticator", which is an invariant face recognition system that uses intelligent mobile agents. This system can provide fully automatic, mobile and reliable user authentication. More importantly, the authentication process can be carried out without the users necessarily being aware of it. Experimental results are presented for a database of 1020 tested face images obtained under conditions of widely varying facial expressions, viewing perspectives and image sizes. An overall average correct recognition rate of over 90% is attained.

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Krithika, L. B. "Face Recognition and Authentication using SIFT." Research Journal of Pharmacy and Technology 10, no. 2 (2017): 401. http://dx.doi.org/10.5958/0974-360x.2017.00081.6.

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17

Khidher, Israa, and Thamir Abdul Hafidh. "Biometrics Identification based Face Image Authentication." JOURNAL OF EDUCATION AND SCIENCE 22, no. 3 (September 1, 2009): 61–74. http://dx.doi.org/10.33899/edusj.2009.57761.

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18

Vybornova, O. N., A. A. Pryhodko, and E. M. Sologubova. "USER AUTHENTICATION BY FACE AND MIMIK." Mathematical Methods in Technologies and Technics, no. 9 (2021): 87–91. http://dx.doi.org/10.52348/2712-8873_mmtt_2021_9_87.

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19

Banerjee, Indradip, Dipankar Chatterjee, Souvik Bhattacharyya, and Gautam Sanyal. "Establishing User Authentication using Face Geometry." International Journal of Computer Applications 92, no. 16 (April 18, 2014): 1–7. http://dx.doi.org/10.5120/16090-5259.

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20

Štruc, Vitomir. "Face authentication using a hybrid approach." Journal of Electronic Imaging 17, no. 1 (January 1, 2008): 011003. http://dx.doi.org/10.1117/1.2885149.

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21

Jhansi Rani, G., G. Shanmukhi Rama, K. Ranganath, Tarun Kumar Juluri, and Ch Vinay Kumar Reddy. "Face detection authentication analysis on smartphones." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 032026. http://dx.doi.org/10.1088/1757-899x/981/3/032026.

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22

Jonsson, K., J. Kittler, Y. P. Li, and J. Matas. "Support vector machines for face authentication." Image and Vision Computing 20, no. 5-6 (April 2002): 369–75. http://dx.doi.org/10.1016/s0262-8856(02)00009-4.

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23

Banerjee, Debdeep, and Kevin Yu. "3D Face Authentication Software Test Automation." IEEE Access 8 (2020): 46546–58. http://dx.doi.org/10.1109/access.2020.2978899.

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V, Veena. "FAMC Face Authentication for Mobile Concurrence." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1389–94. http://dx.doi.org/10.17762/ijritcc2321-8169.1503106.

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25

McCool, Christopher, Roy Wallace, Mitchell McLaren, Laurent El Shafey, and Sébastien Marcel. "Session variability modelling for face authentication." IET Biometrics 2, no. 3 (September 2013): 117–29. http://dx.doi.org/10.1049/iet-bmt.2012.0059.

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26

Abate, Andrea F., Riccardo Distasi, Michele Nappi, and Daniel Riccio. "Face authentication using speed fractal technique." Image and Vision Computing 24, no. 9 (September 2006): 977–86. http://dx.doi.org/10.1016/j.imavis.2006.02.023.

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27

Lin, Tzung-Han, and Wen-Pin Shih. "Automatic face authentication with self compensation." Image and Vision Computing 26, no. 6 (June 2008): 863–70. http://dx.doi.org/10.1016/j.imavis.2007.10.002.

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28

Abusham, Eimad, Basil Ibrahim, Kashif Zia, and Muhammad Rehman. "Facial Image Encryption for Secure Face Recognition System." Electronics 12, no. 3 (February 3, 2023): 774. http://dx.doi.org/10.3390/electronics12030774.

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A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

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Li, Daming, Qinglang Su, Lianbing Deng, and Kaicheng Cai. "3D Reconstruction of Face Image Authentication Technology in Electronic Transaction Authentication." IEEE Sensors Journal 20, no. 20 (October 15, 2020): 11909–18. http://dx.doi.org/10.1109/jsen.2019.2958655.

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Menezes, Tresnor. "Face Recognition Attendance System using Raspberry Pi." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1145–49. http://dx.doi.org/10.22214/ijraset.2021.37499.

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Abstract: Face recognition is used for security, authentication, identification, and has got many advantages over conventional methods. It is being used in many sectors since it is contactless and non-invasive. Billions of images have been uploaded on social media networks and are crawled by search engines over many years. These images may include many different faces. The increase in computing capability and collected data has helped in creating more powerful neural network models. [1] This project thesis aims to create an attendance system which uses face recognition biometric authentication as the currently used manual attendance system is cumbersome to maintain and time consuming. Face recognition prohibits the chance of students marking attendance for their peers (proxy attendance). Keywords: Face Recognition, Face embeddings, Face Detection, Image Processing, Raspberry Pi automation.

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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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Zhu, Dexin, Xiaohui Li, Xiaohong Li, Rongkai Wei, Jianan Wu, and Lijun Song. "A Quantum Identity Authentication Protocol Based on Optical Transmission & Face Recognition." International Journal of Online Engineering (iJOE) 14, no. 04 (April 26, 2018): 58. http://dx.doi.org/10.3991/ijoe.v14i04.8374.

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Specific to security issues concerning identity authentication of mobile applications, a quantum identity authentication protocol based on optical transmission and face recognition is put forward in this paper with consideration of the unconditional security characteristic of quantum key. As for this protocol, optical transmission technology is adopted to acquire quantum key and identity authentication encrypted by quantum key can thus be realized, for which key feature points of face image and user password serve as dual authentication factors. Experimental result and security analysis indicate that this protocol can resist illegal attack and ensure security of identity authentication of mobile applications, which also has great operating efficiency.

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KUMAR, AJAY, and DAVID ZHANG. "USER AUTHENTICATION USING FUSION OF FACE AND PALMPRINT." International Journal of Image and Graphics 09, no. 02 (April 2009): 251–70. http://dx.doi.org/10.1142/s0219467809003423.

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This paper presents a new method of personal authentication using face and palmprint images. The facial and palmprint images can be simultaneously acquired by using a pair of digital camera and integrated to achieve higher confidence in personal authentication. The proposed method of fusion uses a feed-forward neural network to integrate individual matching scores and generate a combined decision score. The significance of the proposed method is more than improving performance for bimodal system. Our method uses the claimed identity of users as a feature for fusion. Thus the required weights and bias on individual biometric matching scores are automatically computed to achieve the best possible performance. The experimental results also demonstrate that Sum, Max, and Product rule can be used to achieve significant performance improvement when consolidated matching scores are employed instead of direct matching scores. The fusion strategy used in this paper outperforms even its existing facial and palmprint modules. The performance indices for personal authentication system using two-class separation criterion functions have been analyzed and evaluated. The method proposed in this paper can be extended for any multimodal authentication system to achieve higher performance.

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Zeng, Ying, Qunjian Wu, Kai Yang, Li Tong, Bin Yan, Jun Shu, and Dezhong Yao. "EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels." Sensors 19, no. 1 (December 20, 2018): 6. http://dx.doi.org/10.3390/s19010006.

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Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time.

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Qian, Mingzhu, and Xiaobao Wang. "Cloud Data Access Prevention Method in Face Recognition Technology Based on Computer Vision." Security and Communication Networks 2022 (June 2, 2022): 1–14. http://dx.doi.org/10.1155/2022/5803026.

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With the development of cloud technology and the innovation of information network technology, people’s dependence on the network has gradually increased, and there are some loopholes in cloud data access. The traditional account permission model can no longer meet the conditions of cloud data access alone. If the visitor temporarily leaves the computer or goes out in an emergency, the data are likely to be leaked. Based on the importance and concern of this issue, some scholars have proposed an authentication system combined with biometric face recognition, but the traditional face recognition system has certain security risks. Such as using face pictures and videos to deceive the system, tampering with face templates, etc. Based on this, this paper proposes an encrypted face authentication system based on CNN neural network. Through the authentication of face data, the content transmission of each part is carried out in the form of ciphertext to ensure the security of information. The experimental results in this paper show that the authentication accuracy rate of DeepID is 94% when it is not encrypted, and the authentication accuracy rate decreases slightly after encryption, which is 93.3%. It is similar in other cases. When the network structure and data set remain unchanged, encryption reduces the authentication accuracy rate by 0.3%–2.4%. It can be seen that the scheme proposed in this chapter improves the system security at the cost of a smaller accuracy rate.

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Lee, Yongjin. "Secure Face Authentication Framework in Open Networks." ETRI Journal 32, no. 6 (December 6, 2010): 950–60. http://dx.doi.org/10.4218/etrij.10.1510.0103.

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37

Jain, Ayush. "Secure Authentication for Banking Using Face Recognition." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 2, 2021): 1–8. http://dx.doi.org/10.54060/jieee/002.02.001.

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With the increasing demand for online banking lack of security in the system has been felt due to a tremendous increase in fraudulent activities. Facial recognition is one of the numerous ways that banks can increase security and accessibility. This paper proposes to inspect the use of facial recognition for login and for banking purposes. The potency of our system is that it provides strong security, username and password verification, face recognition and pin for a successful transaction. Multilevel Security of this system will reduce problems of cyber-crime and maintain the safety of the internet banking system. The end result is a strengthened authentication system that will escalate the confidence of customers in the banking sector.

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Sayed, Mohamed, and Faris Baker. "Thermal Face Authentication with Convolutional Neural Network." Journal of Computer Science 14, no. 12 (December 1, 2018): 1627–37. http://dx.doi.org/10.3844/jcssp.2018.1627.1637.

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39

Liu, Xiaoming, Tsuhan Chen, and B. V. K. Vijaya Kumar. "Face authentication for multiple subjects using eigenflow." Pattern Recognition 36, no. 2 (February 2003): 313–28. http://dx.doi.org/10.1016/s0031-3203(02)00033-x.

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40

De Marsico, Maria, Michele Nappi, and Daniel Riccio. "Face authentication with undercontrolled pose and illumination." Signal, Image and Video Processing 5, no. 4 (August 7, 2011): 401–13. http://dx.doi.org/10.1007/s11760-011-0244-6.

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41

Vazquez-Fernandez, Esteban, and Daniel Gonzalez-Jimenez. "Face recognition for authentication on mobile devices." Image and Vision Computing 55 (November 2016): 31–33. http://dx.doi.org/10.1016/j.imavis.2016.03.018.

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42

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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Angadi, Shanmukhappa A., and Sanjeevakumar M. Hatture. "Face Recognition Through Symbolic Modeling of Face Graphs and Texture." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 12 (November 2019): 1956008. http://dx.doi.org/10.1142/s0218001419560081.

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Face recognition helps in authentication of the user using remotely acquired facial information. The dynamic nature of face images like pose, illumination, expression, occlusion, aging, etc. degrades the performance of the face recognition system. In this paper, a new face recognition system using facial images with illumination variation, pose variation and partial occlusion is presented. The facial image is described as a collection of three complete connected graphs and these graphs are represented as symbolic objects. The structural characteristics, i.e. graph spectral properties, energy of graph, are extracted and embedded in a symbolic object. The texture features from the cheeks portions are extracted using center symmetric local binary pattern (CS-LBP) descriptor. The global features of the face image, i.e. length and width, are also extracted. Further symbolic data structure is constructed using the above features, namely, the graph spectral properties, energy of graph, global features and texture features. User authentication is performed using a new symbolic similarity metric. The performance is investigated by conducting the experiments with AR face database and VTU-BEC-DB multimodal database. The experimental results demonstrate an identification rate of 95.97% and 97.20% for the two databases.

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Alzu’bi, Ahmad, Firas Albalas, Tawfik AL-Hadhrami, Lojin Bani Younis, and Amjad Bashayreh. "Masked Face Recognition Using Deep Learning: A Review." Electronics 10, no. 21 (October 31, 2021): 2666. http://dx.doi.org/10.3390/electronics10212666.

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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Ximenes, Agostinho Marques, Sritrusta Sukaridhoto, Amang Sudarsono, and Hasan Basri. "Mobile Platform Biometric Cloud Authentication." INTEK: Jurnal Penelitian 6, no. 2 (November 12, 2019): 75. http://dx.doi.org/10.31963/intek.v6i2.1525.

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Berdasarkan data Pusat Statistik Indonesia, tingkat kemiskinan pada bulan September 2018 adalah 25,95 juta, berdasrkan data tingkat kemiskinan masyarakat tersebut pemeritnah menyalurkan dana bantuan mengatasi tingkat kemiskinan masyarakat melalui Bank. Namun, Bank tidak dapat mengalokasikan dana karena biaya untuk membangun infrastruktur mahal, seperti membuat ATM.Berbagai kendala tersebut, Bank perlu menemukan solusi baru agar dapat mengalokasikan dana kepada masyrakat dengan biaya yang murah, Mobile Platform Biometric Cloud Authentication adalah salah satu solusi. Dalam penelitian ini, eksperimen yang dilakukan melakukan autentikasi dengan QR Code Scan dan face recognize (data face dienkripsi dan didekripsi dengan kritografi algoritma AES 256 bit). Konsentrasi penelitian ini terletak pada eksperimen terhadap komunikasi keamanan data transaksi payment merchant onlie degan QR Code scan dan Face recognize yang berbasis mobile android dan serta spesfikasi android versi 23. Hasil pengujian pada aplikasi Mobile ini menunjukkan bahwa QR Code scan dan face recognize dapat diimplementasikan pada transaksi payment merchant online dengan akurasi 95% dan membutuhkan 53, 21 detik per transaksi.

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Abusham, Eimad, Basil Ibrahim, Kashif Zia, and Sanad Al Maskari. "An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System." Scientific Programming 2022 (November 25, 2022): 1–17. http://dx.doi.org/10.1155/2022/2628885.

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Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined by u 2 s i z e A 0 where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.

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Anand, Diksha, and Kamal Gupta. "Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 51. http://dx.doi.org/10.23956/ijarcsse.v8i6.722.

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Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

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Banh, Quoc Tuan, Hong Minh Le, Hoang Dat Luu, Hai Long Ngo, Khac Thien Cao, Viet Hoang Do, and Van Binh Le. "Research and development of a two-factors face authentication system using an invisible F-QR code on ID card by 1064 nm laser and AI camera." Ministry of Science and Technology, Vietnam 64, no. 10 (October 12, 2022): 33–37. http://dx.doi.org/10.31276/vjst.64(10db).33-37.

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The face authentication system using two security factors: an invisible F-QR (face QR) code printed on an ID card and face recognition using AI camera is presented in this paper. F-QR codes with facial features are generated by a chain processing of face detection, face property extraction and QR code generation. The F-QR code is printed on a plastic ID card using a 1064 nm fiber laser. The ID card is made of special materials, including a layer of filter material that allows the 1064 nm laser to pass through while blocking visible lights so that the printed F-QR code on the card is invisible to the human eye or conventional QR code readers. Face authentication is implemented using a dedicated invisible F-QR code reader combined with an AI camera to decide the Cosine similarity between two face feature vectors to make an accurate decision for the authentication.

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Mehta, Miti. "Online Voting System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1471–76. http://dx.doi.org/10.22214/ijraset.2022.42552.

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Abstract: The ‘Online Voting System’ is a web based voting platform for conducting elections online. This system seeks to use face recognition algorithm for voter identity authentication to enhance the security of the electioneering process and ultimately providing an online platform which enables all eligible voters to exercise this activity from any location. The user must sign in/login using their respective credentials and they will be logged in into the system only after the face recognition authentication is successful. Thereafter, the voter can cast their vote securely and logout of the system. Hence, this project based on Online Voting System could be used for conducting secure and fair elections online. Keywords: online voting, face detection, face recognition, authentication, django, tensorflow.js, deepface

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Hussain, Tahir, Dostdar Hussain, Israr Hussain, Hussain AlSalman, Saddam Hussain, Syed Sajid Ullah, and Suheer Al-Hadhrami. "Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems." Computational and Mathematical Methods in Medicine 2022 (February 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/5137513.

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Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.

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