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Journal articles on the topic 'Face verification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Zhang, Xue Zhi, Xiao Kang Tang, Qiong Zou, Yong Zhen Zhang, and Da Wei Zhang. "Video Based Face Verification." Applied Mechanics and Materials 556-562 (May 2014): 4893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4893.

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A method of PCA face verification based fuzzy membership is proposed. Constructing the face gallery set through video streaming, using principal component analysis to feature extraction and designing a classifier based fuzzy membership. To verify face in accordance with the threshold principle of fuzzy pattern recognition. The method is compared to the method of PCA face verification, experimental results shows that the proposed method has higher accuracy and robustness.

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Chmielińska, Jolanta, and Jacek Jakubowski. "Biometrical driver face verification." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 6 (2018): 68–72. http://dx.doi.org/10.24136/atest.2018.039.

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The paper discusses the problem of face verification in a driver monitoring system for the purpose of traffic safety. Two different methods of face verification were proposed. Both of them are based on a convolutional neural network and were developed with the use of a transfer learning technique. In the paper, the results produced by both proposed method have been presented and compared. Moreover, their advantages and disadvantages have been discussed.

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BEBIS, GEORGE, SATISHKUMAR UTHIRAM, and MICHAEL GEORGIOPOULOS. "FACE DETECTION AND VERIFICATION USING GENETIC SEARCH." International Journal on Artificial Intelligence Tools 09, no. 02 (2000): 225–46. http://dx.doi.org/10.1142/s0218213000000161.

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We consider the problem of searching for the face of a particular individual in a two-dimensional intensity image. This problem has many potential applications such as locating a person in a crowd using images obtained by surveillance cameras. There are two steps in solving this problem: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against a face of interest (face verification). Without any a-priori knowledge about the location and size of a face in an image, every possible image location and face size should be considere

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Grudzień, Artur, Marcin Kowalski, and Norbert Pałka. "Thermal Face Verification through Identification." Sensors 21, no. 9 (2021): 3301. http://dx.doi.org/10.3390/s21093301.

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This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe

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Manda, Bappaditya, Xudong Jiang, and Alex Kot. "Face Verification Using Modeled Eigenspectrum." Open Artificial Intelligence Journal 2, no. 1 (2008): 35–45. http://dx.doi.org/10.2174/1874061800802010035.

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Face verification is different from face identification task. Some traditional subspace methods that work well in face identification may suffer from severe over-fitting problem when applied for the verification task. Conventional discriminative methods such as linear discriminant analysis (LDA) and its variants are highly sensitive to the training data, which hinders them from achieving high verification accuracy. This work proposes an eigenspectrum model that alleviates the over-fitting problems by replacing the unreliable small and zero eigenvalues with the model values. It also enables the

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Sao, Anil Kumar, and B. Yegnanarayana. "Face Verification Using Template Matching." IEEE Transactions on Information Forensics and Security 2, no. 3 (2007): 636–41. http://dx.doi.org/10.1109/tifs.2007.902920.

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7

Troncoso-Pastoriza, J. R., D. Gonzalez-Jimenez, and F. Perez-Gonzalez. "Fully Private Noninteractive Face Verification." IEEE Transactions on Information Forensics and Security 8, no. 7 (2013): 1101–14. http://dx.doi.org/10.1109/tifs.2013.2262273.

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8

Matas, J., K. Jonsson, and J. Kittler. "Fast face localisation and verification." Image and Vision Computing 17, no. 8 (1999): 575–81. http://dx.doi.org/10.1016/s0262-8856(98)00176-0.

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9

Jang, Jun-Su, Kuk-Hyun Han, and Jong-Hwan Kim. "Evolutionary algorithm-based face verification." Pattern Recognition Letters 25, no. 16 (2004): 1857–65. http://dx.doi.org/10.1016/j.patrec.2004.08.013.

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10

Dhomne, Amit, and Pankaj Kumar Sa. "Face Verification Using Deep Learning." JIMS8I � International Journal of Information Communication and Computing Technology 6, no. 1 (2018): 332. http://dx.doi.org/10.5958/2347-7202.2018.00003.8.

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11

Ramana, N., and R. Chellappa. "Face Verification Across Age Progression." IEEE Transactions on Image Processing 15, no. 11 (2006): 3349–61. http://dx.doi.org/10.1109/tip.2006.881993.

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12

Yan, Shuicheng, Dong Xu, and Xiaoou Tang. "Face Verification With Balanced Thresholds." IEEE Transactions on Image Processing 16, no. 1 (2007): 262–68. http://dx.doi.org/10.1109/tip.2006.884939.

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13

Lin, Yu-Sheng, Zhe-Yu Liu, Yu-An Chen, Yu-Siang Wang, Ya-Liang Chang, and Winston H. Hsu. "xCos: An Explainable Cosine Metric for Face Verification Task." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3s (2021): 1–16. http://dx.doi.org/10.1145/3469288.

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We study the XAI (explainable AI) on the face recognition task, particularly the face verification. Face verification has become a crucial task in recent days and it has been deployed to plenty of applications, such as access control, surveillance, and automatic personal log-on for mobile devices. With the increasing amount of data, deep convolutional neural networks can achieve very high accuracy for the face verification task. Beyond exceptional performances, deep face verification models need more interpretability so that we can trust the results they generate. In this article, we propose a

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14

Chinapas, Adulwit, Pattarawit Polpinit, Narong Intiruk, and Kanda Runapongsa Saikaew. "Personal Verification System Using ID Card and Face Photo." International Journal of Machine Learning and Computing 9, no. 4 (2019): 407–12. http://dx.doi.org/10.18178/ijmlc.2019.9.4.818.

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15

Hahn, Vedrana Krivokuca, and Sebastien Marcel. "Towards Protecting Face Embeddings in Mobile Face Verification Scenarios." IEEE Transactions on Biometrics, Behavior, and Identity Science 4, no. 1 (2022): 117–34. http://dx.doi.org/10.1109/tbiom.2022.3140472.

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16

Wu, Fuzhang, Yan Kong, Weiming Dong, and Yanjun Wu. "Gradient-aware blind face inpainting for deep face verification." Neurocomputing 331 (February 2019): 301–11. http://dx.doi.org/10.1016/j.neucom.2018.11.073.

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17

Li, Baijia. "The current situation and potential development of face recognition." Applied and Computational Engineering 4, no. 1 (2023): 308–16. http://dx.doi.org/10.54254/2755-2721/4/20230478.

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Face recognition has received more attention in the recent past. It refers to using biometric technology to identify individuals from a captured image by comparing it to the images in the database. There are three face recognition techniques: 2D, 2D-3D and 3D. Face recognition occurs in three processes. Firstly, face recognition begins with face detection, where an image is identified as having a face. That is followed by face extraction, which involves identifying the various faces within an image. The final stage is face classification which entails face verification or face identification.

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18

Gurumurthy, Sasikumar. "Age Estimation and Gender Classification based on Face detection and feature extraction." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 1 (2013): 134–40. http://dx.doi.org/10.24297/ijmit.v4i1.809.

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Nowadays the computer systems created a various types of automated applications in personal identification like biometrics, face recognition techniques. Face verification has turn into an area of dynamic research and the applications are important in law enforcement because it can be done without involving the subject. Still, the influence of age estimation on face verification become a challenge to decide the similarity of pair images from individual faces considering very limited of data base availability. We focus on the development of image processing and face detection on face verificatio

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19

Leszczyński, Mariusz. "Image Preprocessing for Illumination Invariant Face Verification." Journal of Telecommunications and Information Technology, no. 4 (June 27, 2023): 19–25. http://dx.doi.org/10.26636/jtit.2010.4.1092.

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Performance of the face verification system depend on many conditions. One of the most problematic is varying illumination condition. In this paper 14 normalization algorithms based on histogram normalization, illumination properties and the human perception theory were compared using 3 verification methods. The results obtained from the experiments showed that the illumination preprocessing methods significantly improves the verification rate and it’s a very important step in face verification system.

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20

Li, Peiqin, Jianbin Xie, Wei Yan, Zhen Li, and Gangyao Kuang. "Living Face Verification via Multi-CNNs." International Journal of Computational Intelligence Systems 12, no. 1 (2018): 183. http://dx.doi.org/10.2991/ijcis.2018.125905637.

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21

Huang, Hai, and Luyao Wang. "Efficient privacy-preserving face verification scheme." Journal of Information Security and Applications 63 (December 2021): 103055. http://dx.doi.org/10.1016/j.jisa.2021.103055.

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22

Borghi, Guido, Stefano Pini, Roberto Vezzani, and Rita Cucchiara. "Driver Face Verification with Depth Maps." Sensors 19, no. 15 (2019): 3361. http://dx.doi.org/10.3390/s19153361.

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Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external lig

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23

Seo, Hae Jong, and Peyman Milanfar. "Face Verification Using the LARK Representation." IEEE Transactions on Information Forensics and Security 6, no. 4 (2011): 1275–86. http://dx.doi.org/10.1109/tifs.2011.2159205.

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24

Sun, Yi, Xiaogang Wang, and Xiaoou Tang. "Hybrid Deep Learning for Face Verification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 10 (2016): 1997–2009. http://dx.doi.org/10.1109/tpami.2015.2505293.

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25

Ou, Zongying, Tieming Su, Fan Ou, Jianxin Zhang, and Dianting Liu. "A Mobile-Based Face Verification System." International Journal of Distributed Sensor Networks 5, no. 1 (2009): 12. http://dx.doi.org/10.1080/15501320802505945.

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26

O'Toole, Alice J., HervÉ Abdi, Fang Jiang, and P. Jonathon Phillips. "Fusing Face-Verification Algorithms and Humans." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 5 (2007): 1149–55. http://dx.doi.org/10.1109/tsmcb.2007.907034.

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27

Chih-Hsueh Duan, Chen-Kuo Chiang, and Shang-Hong Lai. "Face Verification With Local Sparse Representation." IEEE Signal Processing Letters 20, no. 2 (2013): 177–80. http://dx.doi.org/10.1109/lsp.2012.2237550.

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28

Wang, Feng, Jian Cheng, Weiyang Liu, and Haijun Liu. "Additive Margin Softmax for Face Verification." IEEE Signal Processing Letters 25, no. 7 (2018): 926–30. http://dx.doi.org/10.1109/lsp.2018.2822810.

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29

Pang, Ying Han, Andrew Beng Jin Teoh, and Fu San Hiew. "Locality Regularization Embedding for face verification." Pattern Recognition 48, no. 1 (2015): 86–102. http://dx.doi.org/10.1016/j.patcog.2014.07.010.

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30

Lee, Kyoung-Mi. "Component-based face detection and verification." Pattern Recognition Letters 29, no. 3 (2008): 200–214. http://dx.doi.org/10.1016/j.patrec.2007.09.013.

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31

Li, Baoxin, and Rama Chellappa. "Face verification through tracking facial features." Journal of the Optical Society of America A 18, no. 12 (2001): 2969. http://dx.doi.org/10.1364/josaa.18.002969.

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32

Zhu, Qi, and Chengli Sun. "Image-based face verification and experiments." Neural Computing and Applications 23, no. 3-4 (2012): 947–56. http://dx.doi.org/10.1007/s00521-012-1019-x.

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33

Salama, Gouda. "IMAGE-BASED HUMAN FACE VERIFICATION MODEL." International Conference on Aerospace Sciences and Aviation Technology 12, ASAT CONFERENCE (2007): 1–10. http://dx.doi.org/10.21608/asat.2007.23987.

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34

Yan, Shuicheng, Jianzhuang Liu, Xiaoou Tang, and Thomas S. Huang. "Formulating Face Verification With Semidefinite Programming." IEEE Transactions on Image Processing 16, no. 11 (2007): 2802–10. http://dx.doi.org/10.1109/tip.2007.906271.

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35

Wallace, R., and M. McLaren. "Total variability modelling for face verification." IET Biometrics 1, no. 4 (2012): 188–99. http://dx.doi.org/10.1049/iet-bmt.2012.0024.

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36

López‐López, Eric, Xosé M. Pardo, Carlos V. Regueiro, Roberto Iglesias, and Fernando E. Casado. "Dataset bias exposed in face verification." IET Biometrics 8, no. 4 (2019): 249–58. http://dx.doi.org/10.1049/iet-bmt.2018.5224.

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37

Mundhe, Mr Kunal P. "ATM Security based on Face Verification." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3171–76. http://dx.doi.org/10.22214/ijraset.2024.59589.

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Abstract: To provide a reliable security solution, this project focuses on creating an ATM security system based on facial recognition using OpenCV, machine learning, and deep learning. The paper examines how facial recognition technology can improve ATM security, offering a non-intrusive and highly accurate method of identity verification. By analyzing unique facial features, such as facial component sizes and shapes, this technology can authenticate users in real-time reliably. The proposed system integrates facial recognition software with existing ATM infrastructure. Users are prompted to

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38

MR.P.C.Karthik, Deepthi M.Sai, and K.Abhiram. "Secure Connected Transactions using Face Verification." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 165–67. https://doi.org/10.35940/ijeat.D8023.069520.

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In this day and age, cash can be required whenever or anyplace, for example, shopping, voyaging or wellbeing crises and so on. That additionally expands the danger of getting robed. Bank is a most secure spot to keep cash. In any case, consider the possibility that somebody will take your card and by one way or another he/she will know your secret key, it will give him/her full access to your cash. According to the present situation the online exchange is secure with one time secret word (OTP). In age of OTP there are numerous variables that can make OTP special each time it is produced. Right

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Hassan, Norsalina, Dzati Athiar Ramli, and Shahrel Azmin Suandi. "Fusion of Face and Fingerprint for Robust Personal Verification System." International Journal of Machine Learning and Computing 4, no. 4 (2014): 371–75. http://dx.doi.org/10.7763/ijmlc.2014.v4.439.

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40

Huang, Xixian, Xiongjun Zeng, Qingxiang Wu, Yu Lu, Xi Huang, and Hua Zheng. "Face Verification Based on Deep Learning for Person Tracking in Hazardous Goods Factories." Processes 10, no. 2 (2022): 380. http://dx.doi.org/10.3390/pr10020380.

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Person tracking in hazardous goods factories can provide a significant improvement in security and safety. This article proposes a face verification model which can be used to record travel paths for staff or related persons in the factory. As face images are captured from the dynamic crowd at entrance–exit gates of workshops, face verification is challenged by polymorphic faces, poor illumination and changing of a person’s pose. To adapt to this situation, a new face verification model is proposed, which is composed of two advanced deep learning neural network models. Firstly, MTCNN (Multi-Ta

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41

Lu, Qiuju, and Peipei Gan. "Low-Light Face Recognition and Identity Verification Based on Image Enhancement." Traitement du Signal 39, no. 2 (2022): 513–19. http://dx.doi.org/10.18280/ts.390213.

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After years of development, face recognition is now a relatively perfect technology. It is non-contact, intuitive, simple, accurate, and applicable to complex practical environments. To a certain extent, the application of deep learning has enhanced the accuracy of face recognition. But there are some defects with deep learning in detecting face objects of different types in different environments, calling for further explorations. Therefore, this paper explores the low-light face recognition and identity verification based on image enhancement. Specifically, light processing and Gaussian filt

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42

V., Vibavan, Gayathri S., and Saranya K. "AUTOMATED VOTERS VERIFICATION SYSTEM THROUGH FACIAL RECOGNITION." International Journal of Advanced Trends in Engineering and Technology 1, no. 2 (2017): 49–52. https://doi.org/10.5281/zenodo.345732.

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The automated voter verification system using face recognition aims to replace the manual verification of citizens during electoral polling. This system was designed with the objective to automatically identify each individual who votes, by capturing images and comparing the image with stored database image files. Extraction methods viz. PCA (Principal Component Analysis) –Thus we create a feature set for each of the images provided in the database. During real time, the images of human face may be extracted from a USB camera. The system will generate an internal list of the voters and non-vot

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43

Ene, Princewill Chigozie. "Fingerprint and Face Recognition System using A Feed-Forward Artificial Neural Network Paradigm." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 1004–15. https://doi.org/10.5281/zenodo.7055795.

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This research presents the development of intelligent techniques for fingerprint and faces recognition systems. This was achieved following data collection, data acquisition, data processing, artificial intelligence, training, and result presentation. The intelligent technique was modeled using the structural method to develop the algorithm for face and fingerprint verification systems. The algorithms were implemented with Simulink. The result showed that the average Means Squre Error(MSE) for face is 4.7E-05Mu, that for the fingerprint is 2.05E-05; the regression value for face is 0.973 and 0

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44

Tamimi, Abdelfatah Aref, Omaima Nazar Al-Allaf, and Mohammad Ahmad Alia. "Eigen Faces and Principle Component Analysis for Face Recognition Systems: A Comparative Study." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 4 (2015): 5650–60. http://dx.doi.org/10.24297/ijct.v14i4.1967.

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Face recognition has been largely used in biometric field as a security measure at air ports, passport verification, criminals' list verification, visa processing, and so on. Various literature studies suggested different approaches for face recognition systems and most of these studies have limitations with low performance rates. Eigenfaces and principle component analysis (PCA) can be considered as most important face recognition approaches in the literature. There is a need to develop algorithms and approaches that overcome these disadvantages and improve performance of face recognition sys

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45

Mr.T.R.Gupta, K.Jagadeesh, D.Gopichand, E.Mahalakshmi, and B.Dhanush. "Face Recognition Using Convolutional Neural Network." international journal of engineering technology and management sciences 8, no. 3 (2024): 131–39. http://dx.doi.org/10.46647/ijetms.2024.v08i03.016.

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This study proposes a Real-time Face Recognition leveraging deep learning techniques to address the computational constraints and processing speed requirements of such applications. This model focuses on efficient feature extraction and comparison mechanisms to enable rapid and accurate face verification in real-time scenarios. By incorporating techniques like transfer learning and model compression, the proposed network achieves a balance between accuracy and computational efficiency. The utilization of deep learning enables the network to automatically learn discriminative features from faci

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46

R, Arthi, Manoj Kumar D, Aravindan C, and Vinoth Kumar G. "An Intelligent Quad Level Digital Lock System for Safety Vaults." Indian Journal of Science and Technology 16, no. 38 (2023): 3195–204. https://doi.org/10.17485/IJST/v16i38.1590.

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Abstract <strong>Objectives:</strong> The current authentication system relates to the wellbeing of safety and security through individual verification of OTP, face or email. The proposed framework guarantees through four level verification such as face recognition, QR code access, OTP and a valid email authentication to access the security lock.<strong> Method:</strong> The methodology uses four step verification process of one time password, password authentication, face recognition of the user and authenticated verification of the user.<strong> Findings:</strong> Th

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47

Shinde, Sejal. "Face Recognition Based Attendance System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3911–18. http://dx.doi.org/10.22214/ijraset.2024.60784.

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Abstract: In the field of image analysis and computer vision, one of the most arduous tasks presently considered is Face recognition. The biometric system which basically works on the principle of face recognition is used for the identification or verification of a person from a digitalized image preferably used in surveillance, security and attendance purpose. Face Recognition is becoming more popular than other biometric verification methods due to its simplicity, non-invasiveness, and lack of touch. The system’s major goal is to identify and recognize faces in a real-time environment, match

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48

Solomon, Enoch, Abraham Woubie, and Krzysztof J. Cios. "UFace: An Unsupervised Deep Learning Face Verification System." Electronics 11, no. 23 (2022): 3909. http://dx.doi.org/10.3390/electronics11233909.

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Deep convolutional neural networks are often used for image verification but require large amounts of labeled training data, which are not always available. To address this problem, an unsupervised deep learning face verification system, called UFace, is proposed here. It starts by selecting from large unlabeled data the k most similar and k most dissimilar images to a given face image and uses them for training. UFace is implemented using methods of the autoencoder and Siamese network; the latter is used in all comparisons as its performance is better. Unlike in typical deep neural network tr

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49

Li, Decai, and Xingguo Jiang. "Kinship Verification Method of Face Image Deep Feature Fusion." Academic Journal of Science and Technology 5, no. 1 (2023): 57–62. http://dx.doi.org/10.54097/ajst.v5i1.5348.

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Kinship verification is an important and challenging problem in computer vision. How to extract discriminative features is the key to improve the accuracy of kinship verification. At present, convolutional neural networks (CNNs) for feature extraction in the field of computer vision has achieved remarkable success, making it the most scholars used to study kinship verification related issues. However, few people use the self-attention mechanism with global capture capability to build a backbone feature classification network. Therefore, this paper proposes a backbone feature extraction network

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Zaman, Fadhlan Hafizhelmi Kamaru, Juliana Johari, and Ahmad Ihsan Mohd Yassin. "Learning face similarities for face verification using hybrid convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1333. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1333-1342.

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<span>Face verification focuses on the task of determining whether two face images belong to the same identity or not. For unrestricted faces in the wild, this is a very challenging task. Besides significant degradation due to images that have large variations in pose, illumination, expression, aging, and occlusions, it also suffers from large-scale ever-expanding data needed to perform one-to-many recognition task. In this paper, we propose a face verification method by learning face similarities using a Convolutional Neural Networks (ConvNet). Instead of extracting features from each f

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