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Journal articles on the topic 'Face verification'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 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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3

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 considered, leading to a very large search space. In this paper, we propose using Genetic Algorithms (GAs) for searching the image efficiently. Specifically, we use GAs to find image sub-windows that contain faces and in particular, the face of interest. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. A set of increasingly complex scenes demonstrate the performance of the proposed genetic-search approach.

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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 that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

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5

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 discriminant evaluation in the whole space to extract the low dimensional features effectively. The proposed approach is evaluated and compared with 8 popular subspace based methods for a face verification task. Experimental results on three face databases show that the proposed method consistently outperforms others.

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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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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 novel similarity metric, called explainable cosine ( xCos ), that comes with a learnable module that can be plugged into most of the verification models to provide meaningful explanations. With the help of xCos , we can see which parts of the two input faces are similar, where the model pays its attention to, and how the local similarities are weighted to form the output xCos score. We demonstrate the effectiveness of our proposed method on LFW and various competitive benchmarks, not only resulting in providing novel and desirable model interpretability for face verification but also ensuring the accuracy as plugging into existing face recognition models.

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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. Depending on the type of system, face recognition can either occur in verification or identification mode. Additionally, face recognition has various applications in the current global environment. Face recognition can be used in security systems, hospitals, schools, and retail industries. It allows easier verification and identification of individuals. However, despite the development of the technology, there are still some challenges, such as plastic surgery, illumination, aging, occlusion and pose variation.

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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 verification system by improving the quality of image quality. The main objective of the system is to compare the image with the reference images stored as templates in the database and to determine the age and gender.

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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 light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.

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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 look into a camera for facial authentication when approaching the ATM. Upon a successful match, users gain access to ATM functionalities, ensuring a seamless and secure transaction experience. This ATM security system emphasizes robust user verification before granting financial transaction access. By integrating face recognition and personal question verification, the system provides a multilayered security approach, increasing user confidence and deterring unauthorized access attempts.

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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 now execute client Identification utilizing Face Recognition to confirm the client. If there should be an occurrence of crisis circumstance the login should be possible utilizing OTP and furthermore the individual picture is caught and Mail to the Account Holder. Accordingly our framework is improve in Security contrasting with the current System.

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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-Task Cascaded Convolutional Neural Network) is used to construct a face detector. Based on the SphereFace-20 network model, we have reconstructed a convolutional network architecture with the embedded Batch Normalization elements and the optimized network parameters. The new model, which is called the MDCNN, is used to extract efficient face features. A set of specific processing algorithms is used in the model to process polymorphic face images. The multi-view faces and various types of face images are used to train the models. The experimental results have demonstrated that the proposed model outperforms most existing methods on benchmark datasets such as the Labeled Faces in the Wild (LFW) and YouTube Face (YTF) datasets without multi-view (accuracy is 99.38% and 94.30%, respectively) and the CNBC/FERET datasets with multi-view (accuracy is 94.69%).

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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 filtering were adopted to suppress and eliminate the low-light effect of low-light face images. The basic framework and objective function of the existing generative adversarial network (GAN) were modified. By learning the mapping of side and front faces in multi-pose face images in the image space, a cross-pose GAN was established to turn faces of different poses into front faces. The proposed model was proved effective through experiments.

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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-voters during election. Better accuracy is attained in results and the system takes into account the changes that occurs in the face over the period of time. This system will reduce human efforts and also time required to verify the voters individually and also to eradicate the fraudulent voting. This paper will first audit the related works in the field of participation administration and face acknowledgment.

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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 0.995 for the finger. The algorithm was deployed as a face and fingerprint verification system and the result were tested and validated using a tenfold cross-validation approach. An accuracy of 98.6% was achieved for face recognition and 98.87% was achieved for fingerprint verification results. The performance was compared with other algorithms and it was observed that the new algorithm performs better.

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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 facial images, enhancing the overall verification performance. Additionally, the lightweight design of the network ensures minimal resource consumption, making it suitable for deployment on low-power devices without compromising on performance. Experimental results demonstrate the effectiveness of the proposed model in achieving real-time face verification while maintaining high accuracy levels. Overall, the model presented in this study offers a promising solution for real-time face verification applications where speed and efficiency are crucial factors, showcasing the potential of deep learning techniques in enhancing biometric authentication systems.

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45

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 systems. At the same time, there is a lack of literature studies which are related to face recognition systems based on EigenFaces and PCA. Therefore, this work includes a comparative study of literature researches related to Eigenfaces and PCA for face recognition systems. The main steps, strengths and limitations of each study will be discussed. Many recommendations were suggested in this study.

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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> The significant findings of the proposed computerized code lock framework in the places of the client has been characterized and server produced digits in addition to client that makes figuring out or hacking the code more intricate and furthermore tedious. <strong>Novelty :</strong> The proposed work subsequently describes the novelty of an advanced code lock security framework wherein the client has been granted access with the support of four stages of authentication. The various stages of the authentication includes verification of face recognition, QR code access, OTP generation and valid email verification. <strong>Keywords:</strong> Safety Vaults; OTP; QR Code; Face Recognition  

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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 them with data in the database, and record their attendance. This is intended to make the time-consuming manual attendance process more efficient. The main building block on which all automated systems concerned with human faces are designed is face detection. In many person-system user interfaces, precise face identification is important; the real implication is the precise approach to face identification.

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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 training, UFace computes the loss function k times for similar images and k times for dissimilar images for each input image. UFace’s performance is evaluated using four benchmark face verification datasets: Labeled Faces in the Wild (LFW), YouTube Faces (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP). UFace with the Siamese network achieved accuracies of 99.40%, 96.04%, 95.12% and 97.89%, respectively, on the four datasets. These results are comparable with the state-of-the-art methods, such as ArcFace, GroupFace and MegaFace. The biggest advantage of UFace is that it uses much less training data and does not require labeled data.

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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 face image separately, our ConvNet model jointly extracts relational visual features from two face images in comparison. We train four hybrid ConvNet models to learn how to distinguish similarities between the face pair of four different face portions and join them at top-layer classifier level. We use binary-class classifier at top-layer level to identify the similarity of face pairs which includes a conventional Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), Native Bayes, and another ConvNet. There are 3 face pairing configurations discussed in this paper. Results from experiments using Labeled face in the Wild (LFW) and CelebA datasets indicate that our hybrid ConvNet increases the face verification accuracy by as much as 27% when compared to individual ConvNet approach. We also found that Lateral face pair configuration yields the best LFW test accuracy on a very strict test protocol without any face alignment using MLP as top-layer classifier at 87.89%, which on-par with the state-of-the-arts. We showed that our approach is more flexible in terms of inferencing the learned models on out-of-sample data by testing LFW and CelebA on either model.</span>

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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 model based on a non-convolution, which expands the selection range of traditional classification networks for kinship verification related issues. Specifically, the paper proposes to use Vision Transformers as the basic backbone feature extraction network, combined with CNN with local attention mechanism, to provide a unique integrated solution in kinship verification. The proposed GLANet model is used for kinship verification and can verify 11 kinship pairs. The final experimental results show that in the FIW dataset, compared with the RFIW2020 challenge leading method, the proposed method has better verification effect in kinship, and the accuracy rate can reach 79.6 %.

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