SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.2.25Keywords:
Liveness Detection, Convolutional Neural Network, Face recognition.Dimensions Badge
Issue
Section
License
Copyright (c) 2022 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Face spoofing refers to “tricking” a facial recognition system to gain unauthorized access to a particular system. It is mostly used to steal data and money or spread malware. The malicious impersonation of oneself is a critical component of face spoofing to gain access to a system. It is observed in many identity theft cases, particularly in the financial sector. In 2015, Wen et al. presented experimental results for cutting-edge commercial off-the-shelf face recognition systems. These demonstrated the probability of fake face images being accepted as genuine. The probability could be as high as 70%. Despite this, the vulnerabilities of face recognitionAbstract
systems to attacks were frequently overlooked. The Presentation Attack Detection (PAD) method that determines whether the source of a biometric sample is a live person or a fake representation is known as Liveness Detection. Algorithms are used to accomplish this by analyzing biometric sensor data for the determination of the authenticity of a source.
How to Cite
Downloads
Similar Articles
- R. Kalaiselvi, P. Meenakshi Sundaram, Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Jadhav Girish Vasantrao, Chirag Patel, AT&C and non-technical loss reduction in smart grid using smart metering with AI techniques , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Kavitha V, Panneer Arokiaraj S., RPL-eSOA: Enhancing IoT network sustainability with RPL and enhanced sandpiper optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Deepa Ramachandran VR VR, Kamalraj N, Hybrid deep segmentation architecture using dual attention U-Net and Mask-RCNN for accurate detection of pests, diseases, and weeds in crops , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. Deepa, I.S. Arafat, M. Sathya Priya, S. Saravanan, An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- R Prabhu, S Sathya, P Umaeswari, K Saranya, Lung cancer disease identification using hybrid models , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Swetha Rajkumar, Jayaprasanth Devakumar, LSTM based data driven fault detection and isolation in small modular reactors , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Abhishek Dwivedi, Nikhat Raza Khan, Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
