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shojaeifard, A., Shahrezaee, M., yazdani, H. (2022). Fixing Security Defects of Video Authentication Systems with Face Recognition, Based on 3D Structure and Shape Evaluation. Electronic and Cyber Defense, 9(4), 141-145.
alireza shojaeifard; Mohsen Shahrezaee; hamidreza yazdani. "Fixing Security Defects of Video Authentication Systems with Face Recognition, Based on 3D Structure and Shape Evaluation". Electronic and Cyber Defense, 9, 4, 2022, 141-145.
shojaeifard, A., Shahrezaee, M., yazdani, H. (2022). 'Fixing Security Defects of Video Authentication Systems with Face Recognition, Based on 3D Structure and Shape Evaluation', Electronic and Cyber Defense, 9(4), pp. 141-145.
shojaeifard, A., Shahrezaee, M., yazdani, H. Fixing Security Defects of Video Authentication Systems with Face Recognition, Based on 3D Structure and Shape Evaluation. Electronic and Cyber Defense, 2022; 9(4): 141-145.

Fixing Security Defects of Video Authentication Systems with Face Recognition, Based on 3D Structure and Shape Evaluation

پدافند الکترونیکی و سایبری
Article 11, Volume 9, Issue 4 - Serial Number 36, February 2022, Pages 141-145    PDF (655.35 K)
Document Type: Original Article
Authors
alireza shojaeifard* 1; Mohsen Shahrezaee2; hamidreza yazdani3
1Assistant Professor, Department of Mathematics, Faculty of Basic Sciences, Imam Hossein University, Tehran, Iran
2Assistant Professor, Department of Mathematics and Statistics, Faculty and Research Institute of Basic Sciences, Imam Hossein University, Tehran, Iran
3Collaborating Researcher, Center for Mathematics and Statistics, Research Institute and Faculty of Basic Sciences, Imam Hossein University, Tehran, Iran
Receive Date: 08 September 2021,  Revise Date: 10 October 2021,  Accept Date: 10 October 2021 
Abstract
The weaknesses in video surveillance security systems have encouraged attempts to use hybrid methods to address these issues. In this article, we consider the subject of face recognition based on the evaluation of 3D structure and shape. Evaluating and fitting the structure and achieving a 3D shape makes face recognition possible with a wide range of parameters and under different conditions, and intrinsic and extrinsic models are well isolated from the parameters. Exposure conditions and head positions are all taken into account and well-controlled. As we know, various parameters such as the angle of the head in front of the camera, the direction of imaging, the amount of exposure, and the types of noise in the image are among the most difficulties and issues, affecting the successful detection rate of human face recognition systems. In the field of face recognition, most of the present methods suffer from errors and lack capability when the angle of the head changes especially in the case of profile or three faces, due to face graph extraction and two-dimensional flat structures. The 3D structure of the face overcomes many of these issues. This method is a simultaneous combination of geometry tools, statistical meters, and dimensionality reduction methods. The method accuracy and efficiency testing are based on the correct recognition rate and head position (rotation) .
Keywords
Face recognition; Geometric methods; Dimensionality reduction; Principal component analysis (PCA); Linear discriminant analysis (LDA)
References

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