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Journal articles on the topic 'Software based face antispoofing techniques'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Vinutha, H., and G. Thippeswamy. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. https://doi.org/10.11591/csit.v4i1.pp1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories

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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.p1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: h

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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.pp1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: ha

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V. Chakole, Vijay, and Swati R. Dixit. "MLbFA." International journal of electrical and computer engineering systems 16, no. 4 (2025): 321–32. https://doi.org/10.32985/ijeces.16.4.5.

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The primary aim of the research paper is to deploy an efficient automated face antispoofing system that could consider replay attacks in the presence of partial occlusions. For this purpose, the article introduces a novel machine learning-based face- antispoofing (MLbFA) framework. The system incorporates a modified version of the difference of the Gaussian technique to compute the overall contrast of the input images which is later used to enhance the contrast of the image using contrast correction. On the other hand, the image details, especially the edges are enhanced for significant featur

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Ren, Hanchi, Yi Hu, San Hlaing Myint, et al. "A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems." Wireless Communications and Mobile Computing 2021 (October 31, 2021): 1–15. http://dx.doi.org/10.1155/2021/6711561.

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The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computin

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Shivani, Dugyala. "Attention-Oriented Two-Stream Convolutional Networks for the Identification of Face Spoofing." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 176–81. https://doi.org/10.22214/ijraset.2024.65732.

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Face recognition has been extensively researched and has seen tremendous success in a variety of applications over the past few decades because the human face retains the most information for identifying individuals. Modern face recognition systems are still vulnerable to face spoofing attacks, such as the face video replay attack. Despite the fact that numerous efficient antispoofing techniques have been put forth, we discover that illuminations impair the effectiveness of many of the current techniques. It encourages us to create illumination-invariant anti-spoofing techniques. In this work,

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Akhtar, Zahid, and Gian Luca Foresti. "Face Spoof Attack Recognition Using Discriminative Image Patches." Journal of Electrical and Computer Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4721849.

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Face recognition systems are now being used in many applications such as border crossings, banks, and mobile payments. The wide scale deployment of facial recognition systems has attracted intensive attention to the reliability of face biometrics against spoof attacks, where a photo, a video, or a 3D mask of a genuine user’s face can be used to gain illegitimate access to facilities or services. Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty

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Kadar, Rozita, Jamal Othman, and Naemah Abdul Wahab. "Survey on Ontology-Based Visualization Techniques towards Program Comprehension Application." Journal of Computing Research and Innovation 3, no. 1 (2018): 19–29. http://dx.doi.org/10.24191/jcrinn.v3i1.66.

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A key challenge to software maintainers during performing the software maintenance is to comprehend a source code. Source code plays an important role in aiding software maintainers to get conversant with a software system since some system documentations are often unavailable or outdated. Although there are many researches that have discussed the different strategies and techniques to overcome the program comprehension problem, there exists only a shallow knowledge about the challenges software maintainers face when trying to understand a software system through reading source code. In order

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Deshmukh, Amar B., and N. Usha Rani. "Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution." International Journal of Digital Crime and Forensics 12, no. 3 (2020): 77–95. http://dx.doi.org/10.4018/ijdcf.2020070106.

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One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regressio

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Peng, Peng, Ivens Portugal, Paulo Alencar, and Donald Cowan. "A face recognition software framework based on principal component analysis." PLOS ONE 16, no. 7 (2021): e0254965. http://dx.doi.org/10.1371/journal.pone.0254965.

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Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face

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Barman, Shohag, Hira Lal Gope, M. M. Manjurul Islam, Md Mehedi Hasan, and Umme Salma. "Clustering Techniques for Software Engineering." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (2016): 465. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp465-472.

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<p>Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for ma

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CHEN, PEI, and DAVID SUTER. "SUBSPACE-BASED FACE RECOGNITION: OUTLIER DETECTION AND A NEW DISTANCE CRITERION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (2005): 479–93. http://dx.doi.org/10.1142/s0218001405004174.

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Illumination effects, including shadows and varying lighting, make the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions approximately lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new error criterion for the recogniti

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Yadav, Sujit. "Face Recognition Based Attendance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29402.

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The integration of face recognition technology into attendance management systems has emerged as a transformative solution for monitoring and optimizing attendance processes in educational institutions and organizations. This report presents an in-depth analysis of a project dedicated to the development and implementation of a Face Recognition Attendance System (FRAS) and examines its efficiency and security in various real-world scenarios. The report begins by discussing the significance of attendance tracking systems in educational and corporate environments, highlighting the shortcomings of

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Hachim, Ethar Abdul Wahhab, Yasmin Makki Mohialden, Zeyad Farooq Lutfi, and Nadia Mahmood Hussien. "Green Software Engineering: Cloud-based Face Detection and Static Code Analysis." Aug-Sept 2023, no. 35 (August 16, 2023): 26–34. http://dx.doi.org/10.55529/ijitc.35.26.34.

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This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential

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Rehman, Bacha, Wee Hong Ong, Abby Chee Hong Tan, and Trung Dung Ngo. "Face detection and tracking using hybrid margin-based ROI techniques." Visual Computer 36, no. 3 (2019): 633–47. http://dx.doi.org/10.1007/s00371-019-01649-y.

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Khachouch, Mohamed Karim, Ayoub Korchi, and Younes Lakhrissi. "Architecture Driven Modernization: A Review on Reverse Engineering Techniques based on Models’ Approach." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (October 11, 2023): 293–302. http://dx.doi.org/10.37394/23209.2023.20.32.

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Software specifications represent one of the risks that can cause a project to fail if they tend to be modified during development. it is a problem that all companies with an information system or developing software can face regardless of the latter's size. Specification techniques have indeed evolved over the last few years to avoid this type of situation as much as possible. Nevertheless, one can never predict a client's evolutionary needs. To remedy this problem, there is a solution that we consider effective, which is reverse engineering. Reverse engineering is not a new term. Originally,

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LEE, SANG-WOONG, PATRICK S. P. WANG, SVETLANA N. YANUSHKEVICH, and SEONG-WHAN LEE. "NONITERATIVE 3D FACE RECONSTRUCTION BASED ON PHOTOMETRIC STEREO." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (2008): 389–410. http://dx.doi.org/10.1142/s0218001408006272.

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3D face reconstruction is a popular area within the computer vision domain. 3D face reconstruction should ideally be achieved easily and cost-effectively, without requiring specialized equipment to estimate 3D shapes. As a result of this, many techniques for retrieving 3D shapes from 2D images have been proposed. In this paper, a novel method for 3D face reconstruction based on photometric stereo, which estimates the surface normal from shading information in multiple images, hence recovering the 3D shape of a face, is proposed. In order to overcome the problems of previous approaches related

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Dubovečak, Mario, Emil Dumić, and Andrija Bernik. "Face Detection and Recognition Using Raspberry PI Computer." Tehnički glasnik 17, no. 3 (2023): 346–52. http://dx.doi.org/10.31803/tg-20220321232047.

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This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this pape

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Toderici, George, Sean M. O’Malley, George Passalis, Theoharis Theoharis, and Ioannis A. Kakadiaris. "Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques." International Journal of Computer Vision 89, no. 2-3 (2010): 382–91. http://dx.doi.org/10.1007/s11263-009-0300-7.

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Silviana, Mery, Rina Mirdayanti, Lindawati Lindawati, Wiwin Apriani, and Fitri Yunina. "PELATIHAN PENGGUNAAN SOFTWARE AUDACITY SEBAGAI MEDIA PENGEMAS MATERI AJAR YANG MENARIK." RAMBIDEUN : Jurnal Pengabdian Kepada Masyarakat 5, no. 2 (2022): 151–54. http://dx.doi.org/10.51179/pkm.v5i2.1087.

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Face-to-face restrictions on elementary school students during the pandemic force teachers to have creative and innovative ideas in conducting distance learning. Unlike learning for junior or senior high school students who have better understanding of lessons using simpler methods, elementary school students need more detailed methods of learning. Audacity is an application with script writing and voice recording techniques that are able to help elementary school students develop better understanding in distance lessons. Training on the application of interesting and innovative learning media

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Raden Andy Kurniawan and Umar Zaky. "RADIO FREQUENCY IDENTIFICATION AND IMAGE-BASED FACIAL IDENTIFICATION AS AN EMPLOYEE ATTENDANCE SYSTEM." International Journal of Engineering Technology and Natural Sciences 2, no. 1 (2020): 18–26. http://dx.doi.org/10.46923/ijets.v2i1.67.

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The current development of microcontroller technology can be used to build a presence system for employees. The employee attendance system uses radio frequency identification and facial identification which is designed and built to make it easier to do attendance data recording, so that the data obtained can be precise and accurate. Data collection techniques, namely by interview and observation. The application development process uses the PHP and Python programming languages with Visual Studio Code software applications, Arduino Uno, MySQL software as a database server, and XAMPP as a supp

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S, Haseena, Saroja S, Shri Dharshini D, and Nivetha A. "TVN: Detect Deepfakes Images using Texture Variation Network." Inteligencia Artificial 26, no. 72 (2023): 1–14. http://dx.doi.org/10.4114/intartif.vol26iss72pp1-14.

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Face manipulation technology is rapidly developing, making it impossible for human eyes to recognize fake face photos. Convolutional Neural Network (CNN) discriminators, on the other hand, can fast achieve high accuracy in distinguishing fake/real face photos. In this paper, we look at how CNN models discern between fake and real faces. Face forgery detection relies heavily on Texture Variation Network (TVN) information, according to our findings. We propose a new model, TVN, for robust face fraud detection, based on Convolution and pyramid pooling (PP), as a result of the aforesaid discovery.

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Wasim, Muhammad. "A Comparative Study of Curvature-Based and Differential Versions of Dotter Raster-stereography Techniques." KIET Journal of Computing and Information Sciences 1, no. 1 (2021): 01–12. http://dx.doi.org/10.51153/kjcis.v4i1.37.

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Conventional Line-based Raster-stereography has been a popular technique for 3-D surface topography. However, in its application for human face screening, the problem of line breaking was observed. In order to resolve this problem, there came up a new technique called dotted raster-stereography. The previously reported version of dotted raster-stereography extracted the curvature features of human face. This paper presents a modified version, viz. differential dotted raster-stereography in which instead of curvature, differences in straight line distances between adjacent points are calculated

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Mureno, Althaf Dhava, and Aede Hatib Musta'amal. "Engaging Vocational Students with Construct-Based Games: A Study on Software and Game Development Interest." Letters in Information Technology Education (LITE) 6, no. 2 (2023): 51. https://doi.org/10.17977/um010v6i22023p51-58.

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The transformation of learning in the era of technological disruption and pandemic has caused face-to-face classroom learning to transform into online learning or blended learning. Basic Software and Game Programming lessons have skills in identifying and applying how to develop software and games properly. In the school environment there are still many students experiencing difficulties in fostering interest in learning, this is because the process of honing their skills in the form of media that is less interesting. The focus of this research is to develop a website-based educational game le

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Zhang, Yulin, Yong Hu, and Xiao Chen. "Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing and Multi-Features." Sensors 24, no. 5 (2024): 1351. http://dx.doi.org/10.3390/s24051351.

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With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutiona

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Ghorbel, Ahmed, Walid Aydi, Imen Tajouri, and Nouri Masmoudi. "Hybrid Approach for Face Recognition from a Single Sample per Person by Combining VLC and GOM." Journal of Intelligent Systems 29, no. 1 (2019): 1523–34. http://dx.doi.org/10.1515/jisys-2018-0380.

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Abstract This paper proposes a new face recognition system based on combining two feature extraction techniques: the Vander Lugt correlator (VLC) and Gabor ordinal measures (GOM). The proposed system relies on the execution speed of VLC and the robustness of GOM. In this system, we applied the Tan and Triggs and retina modeling enhancement techniques, which are well suited for VLC and GOM, respectively. We evaluated our system on the standard FERET probe data sets and on extended YaleB database. The obtained results exhibited better face recognition rates in a shorter execution time compared t

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Abhinav, Balasubramanian. "Hybrid Machine Learning Framework For Test Case Generation." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 5 (2021): 1–12. https://doi.org/10.5281/zenodo.14565859.

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Automated test case generation is an essential process in software testing, ensuring effective quality assurance while reducing manual effort. However, existing approaches face challenges such as limited scalability, insufficient test coverage, and difficulty in adapting to evolving software requirements. These issues hinder the effectiveness of both traditional methods and standalone machine learning (ML)-based solutions.To address these challenges, we propose a Hybrid Machine Learning Framework that synergizes rule-based systems with advanced ML techniques. This framework combines the reliab

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Zhou, Binpeng, Junrui Chai, Jing Wang, Heng Zhou, and Lifeng Wen. "An XFEM-Based Analysis of Concrete Face Cracking in Rockfill Dams." Mathematical Problems in Engineering 2021 (July 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/6640668.

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The concrete face of a rockfill dam is a long and thin slab structure, which is highly susceptible to fracture when subjected to the settlement of the dam. The study of the generation and propagation of cracks in the concrete face of rockfill dams is of great significance to dam construction and face crack prevention. In this study, the initiation and propagation of cracks in the concrete face of a rockfill dam are investigated using an extended finite element method (XFEM) and ABAQUS software for the Gongboxia concrete-face rockfill dam. A numerical model for this dam is established using a f

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Mamieva, Dilnoza, Akmalbek Bobomirzaevich Abdusalomov, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo. "Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach." Sensors 23, no. 1 (2023): 502. http://dx.doi.org/10.3390/s23010502.

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Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection s

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Vargas, Hector, and Aldo Martinez. "Hybrid Algorithm for Face Recognition Using an Evolutionary Software Engineering." Applied Mechanics and Materials 423-426 (September 2013): 2543–46. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2543.

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The principal aim was the construction of a face recognition system in order to be implemented in the service robot Donaxi, delimited by the Who is who test which is part of the RoboCups tests set, using an evolutionary development strategy of triple iterations. A two phase hybrid algorithm was developed, the first phase aim was the face detection using the Haar classifiers for face search in an image and the second phase is based on a decision tree whereby the faces characteristics were evaluated by the comparison techniques of phase correlation and histogram comparison. The needed characteri

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Neha, Pradyumna Bora and Dr. Dinesh Chandra Jain. "CAPTCHA Designing techniques and development of CAPTCHA System based on B3AC Algorithm." Scandinavian Journal of Information Systems 35, no. 1 (2023): 707–15. https://doi.org/10.5281/zenodo.7795505.

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Our everyday life now heavily relies on the internet and online security. Most businesses provide their customers internet services. Yet, malicious automated hacking software occasionally attempts to target websites to slow the server. Users must provide their personal information on sign-in or sign-up forms during online registration or booking in order to access and use relevant website functionalities. Only the actual user, who is a human, is expected to register. However, automated hacking programs can also do bandwidth-intensive registrations that slow down or occasionally even shut down

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Mahmoud, Tarek M., Tarek Abd-El-Hafeez, and Ahmed Omar. "A Highly Efficient Content Based Approach to Filter Pornography Websites." International Journal of Computer Vision and Image Processing 2, no. 1 (2012): 75–90. http://dx.doi.org/10.4018/ijcvip.2012010105.

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With the ever-growing Web, the Websites with objectionable contents like pornography, violence, racism, etc. have been augmented rapidly during recent years. Among the offensive contents, the pornography is the most harmful one affecting children safety and causing many destructive side effects. A content filter is one or more pieces of software that work together to prevent users from viewing material found on the Internet. This paper presents an efficient content based software system for detecting and filtering pornography images in Web pages. The proposed system runs online in the backgrou

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SOUNDAR, K. RUBA, and K. MURUGESAN. "CORRELATION BASED FACE MATCHING IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (2010): 1281–94. http://dx.doi.org/10.1142/s021800141000838x.

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Face recognition plays a vital role in authentication, monitoring, indexing, access control and other surveillance applications. Much research on face recognition with various feature based approaches using global or local features employing a number of similarity measurement techniques have been done earlier. Feature based approaches using global features can effectively preserve only the Euclidean structure of face space, that suffer from lack of local features which may play a major role in some applications. On the other hand, wtih local features only the face subspace that best detects th

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Kaburlasos, Vassilis G., Chris Lytridis, Eleni Vrochidou, et al. "Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures." Mathematics 9, no. 22 (2021): 2889. http://dx.doi.org/10.3390/math9222889.

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Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face

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Rajkumar Rajasekaran, Et al. "Data Sharing based on Facial Recognition Clusters." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2351–59. http://dx.doi.org/10.17762/ijritcc.v11i9.9243.

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The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a datab

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Anthwal, Shivangi, and Dinesh Ganotra. "Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions." Revue d'Intelligence Artificielle 34, no. 5 (2020): 585–94. http://dx.doi.org/10.18280/ria.340508.

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Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the

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CHEN, WEI-CHE, and MING-SHI WANG. "REGION-BASED AND CONTENT ADAPTIVE SKIN DETECTION IN COLOR IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 05 (2007): 831–53. http://dx.doi.org/10.1142/s0218001407005715.

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Skin detection plays an important role in applications such as face detection and tracking, person detection and pornography detection. While previous studies focus on pixel-based skin color detection techniques that individually classify each pixel as skin color or non-skin color, this study presents a region-based algorithm for detecting skin color. The proposed algorithm uses a special region, called key skin region, as the basis to classify skin color. A performance comparison with conventional skin classifiers, including the Bayesian classifier, the unimodal Gaussian classifier and the Ga

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Yadav, Rohit, and Raghuraj Singh. "Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance." International Journal of Experimental Research and Review 36 (December 30, 2023): 135–46. http://dx.doi.org/10.52756/ijerr.2023.v36.013.

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Accurate maintenance effort and cost estimation are essential for effective software development. By identifying software modules with poor maintainability, Software Maintainability Prediction (SMP) plays a crucial role in managing software maintenance expenses. Previous research efforts have used multiple regression techniques to predict software maintainability, but the results regarding various accuracy and performance metrics are inconclusive. As such, developing a methodology that can recommend regression techniques for software maintainability prediction in the face of inconsistent perfo

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Kumar Janardan Patra, Et al. "Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 989–98. http://dx.doi.org/10.17762/ijritcc.v11i10.8618.

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Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object

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G S, Monisha, M. Hari Krishnan, Vetri Selvan M, G. Nirmala, and Yogashree G S. "Visual Tracking Based on Human Feature Extraction from Surveillance Video for Human Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 133–41. http://dx.doi.org/10.17762/ijritcc.v11i7.7838.

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A multimodal human identification system based on face and body recognition may be made available for effective biometric authentication. The outcomes are achieved by extracting facial recognition characteristics using several extraction techniques, including Eigen-face and Principle Component Analysis (PCA). Systems for authenticating people using their bodies and faces are implemented using artificial neural networks (ANN) and genetic optimization techniques as classifiers. Through feature fusion and scores fusion, the biometric systems for the human body and face are merged to create a sing

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Almogahed, Abdullah, Hairulnizam Mahdin, Mazni Omar, et al. "A refactoring categorization model for software quality improvement." PLOS ONE 18, no. 11 (2023): e0293742. http://dx.doi.org/10.1371/journal.pone.0293742.

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Refactoring, a widely adopted technique, has proven effective in facilitating and reducing maintenance activities and costs. Nonetheless, the effects of applying refactoring techniques on software quality exhibit inconsistencies and contradictions, leading to conflicting evidence on their overall benefit. Consequently, software developers face challenges in leveraging these techniques to improve software quality. Moreover, the absence of a categorization model hampers developers’ ability to decide the most suitable refactoring techniques for improving software quality, considering specific des

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Tommandru, Suresh, and Domnic Sandanam. "An Automated Framework for Patient Identification and Verification Using Deep Learning." Revue d'Intelligence Artificielle 34, no. 6 (2020): 709–19. http://dx.doi.org/10.18280/ria.340605.

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Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been add

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Raja, Rohit, Tilendra Shishir Sinha, Raj Kumar Patra, and Shrikant Tiwari. "Physiological trait-based biometrical authentication of human-face using LGXP and ANN techniques." International Journal of Information and Computer Security 10, no. 2/3 (2018): 303. http://dx.doi.org/10.1504/ijics.2018.10012575.

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Djauhari, Teuku, and Noni Rahmawati. "Smart Hotel Security: Integrating AI for Advanced Safety Solutions." Internet of Things and Artificial Intelligence Journal 5, no. 1 (2025): 14–26. https://doi.org/10.31763/iota.v5i1.787.

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Security systems are essential for hotels and lodging services, considering that this lodging service is a service that is intended for all people from various circles and from anyone who comes from various places, this is very common and dangerous, in terms of security and comfort. Several security systems are built by hotels, such as CCTV and security systems on doors and various security system techniques such as retina, fingerprint, RFID, and various security techniques. Not only at the door but safety boxes are made with various locking techniques from a combination of numbers. In additio

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Nabat, Zahraa Modher, Mushtaq Talib Mahdi, and Shaymaa Abdul Hussein Shnain. "Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)." Webology 19, no. 1 (2022): 2170–81. http://dx.doi.org/10.14704/web/v19i1/web19147.

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One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector mach

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El Merhebi, Souad, Jean-Christophe Hoelt, Patrice Torguet, and Jean-Pierre Jessel. "Perception-Based Filtering for MMOGs." International Journal of Computer Games Technology 2008 (2008): 1–9. http://dx.doi.org/10.1155/2008/243107.

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Online games have exploded in the last few years. These games face several problems linked to scalability and interactivity. In fact, online games should provide a quick feedback of users' interactions as well as a coherent view of the shared world. However, the search for enhanced scalability dramatically increases message exchange. Such an increase consumes processing power and bandwidth, and thus limits interactivity, consistency, and scalability. To reduce the rate of message exchange, distributed virtual environment systems use filtering techniques such as interest management that filters

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S., Dhanasekaran, V. Anjana Devi, S. Sankara Narayanan, Vijayakarthik P., Hariharasitaraman S., and Rajasekaran S. "A Smart Digital Attendance Monitoring System for Academic Institution Using Machine Learning Techniques." Webology 19, no. 1 (2022): 3109–18. http://dx.doi.org/10.14704/web/v19i1/web19205.

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Face Recognition is the one of the foremost emerging and interesting technology. In this fast-evolving generation, attendance is usually a problem among school community and so as business community. One among the foremost efficient solution for this problem is that the Digital Attendance System. Now a days Computers plays a vital role in many fields. Likewise, in this field also, we will make an efficient use of it. Face detection and recognition-based attendance system may be a process of identifying face of the scholars using image of a face on the idea of high - definition monitor video an

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Ithaya Rani, P., and K. Muneeswaran. "Facial Emotion Recognition Based on Eye and Mouth Regions." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (2016): 1655020. http://dx.doi.org/10.1142/s021800141655020x.

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Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human-computer intelligent interaction nowadays. The eye and the mouth regions are most essential components for facial emotion recognition. Most of the existing approaches have not utilized the eye and the mouth regions for high recognition rate. This paper proposes an approach to overcome this limitation using the eye and the mouth region-based emotion recognition using reinforced local binary patterns (LBP). The local features are extracted in each frame by using Gabor wavelet with selected s

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Yadav, Sujit. "Survey paper on Face Recognition Based Attendance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30710.

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The integration of face recognition technology into attendance management systems has emerged as a transformative solution for monitoring and optimizing attendance processes in educational institutions and organizations. This report presents an in-depth analysis of a project dedicated to the development and implementation of a Face Recognition Attendance System (FRAS) and examines its efficiency and security in various real-world scenarios. The report begins by discussing the significance of attendance tracking systems in educational and corporate environments, highlighting the shortcomings of

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Zagane, Mohammed, Mamdouh Alenezi, and Mustapha Kamel Abdi. "Hybrid Representation to Locate Vulnerable Lines of Code." International Journal of Software Innovation 10, no. 1 (2022): 1–19. http://dx.doi.org/10.4018/ijsi.292020.

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Locating vulnerable lines of code in large software systems needs huge efforts from human experts. This explains the high costs in terms of budget and time needed to correct vulnerabilities. To minimize these costs, automatic solutions of vulnerabilities prediction have been proposed. Existing machine learning (ML)-based solutions face difficulties in predicting vulnerabilities in coarse granularity and in defining suitable code features that limit their effectiveness. To addressee these limitations, in the present work, the authors propose an improved ML-based approach using slice-based code

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