Towards an Effective Approach for Face Recognition with DCGANs Data Augmentation

Ammar, Sirine; Bouwmans, Thierry; Zaghden, Nizar; Néji, Mahmoud

doi:10.1007/978-3-030-64556-4_36

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Some papers used the DCGAN framework to generated images simply as a data augmentation tool with the goal to expand the dataset to optimized training in recognition models. In Lv et al [32], the DCGAN algorithm solves the problem [33] also proposed data augmentation using the DCGAN framework to increase the total number of samples in a face dataset. The proposed method uses the DCGAN framework to generate more face samples as the FaceNet model is used for image classification.…”

Section: Discussionmentioning

confidence: 99%

“…Ammar et al [33] also proposed data augmentation using the DCGAN framework to increase the total number of samples in a face dataset. The proposed method uses the DCGAN framework to generate more face samples as the FaceNet model is used for image classification.…”

Section: Improving Biometric Systemsmentioning

confidence: 99%

“…Discover Artificial Intelligence (2024) 4:42 | https://doi.org/10.1007/s44163-024-00138-z LBP and PCA methods. Compared to the application of the VGG-16 network, the proposed DCGAN and FaceNet-based method also offered higher recognition results [33]. Wang et al [34] proposed the use of the DCGAN framework to generate fake palmprint images for False Acceptance Attacks (FAA).…”

Section: Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Jenkins,

Roy

2024

Discov Artif Intell

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Over the past few years, there has been a proliferation of research in the area of generative adversarial networks (GANs). GANs present a novel approach to producing synthetic data in varying fields including medicine, traffic control, text transferring, image generation, and cybersecurity. To improve the quality of synthetic generation, specifically for images, the GAN technique was paired with convolutional neural networks (CNNs) to build deep convolutional generative adversarial networks (DCGAN). The DCGAN framework is a simple yet stable framework shown to generate quality photorealistic images. There are a number of studies reviewing GANs, providing a comparative analysis of performance, stabilization, and training methods. With respects to the DCGAN architecture, there are literature reviews reporting its usage in forensic sketch to face transformation and fuzzy face recognition. Here, we provide a review detailing the use of the DCGAN framework with biometrics samples for advancements in biometric authentication systems and cybersecurity. As GANs have shown to be a primary tool in generating deepfakes, we explore the use of DCGANs to generating synthetic biometrics that can deceive security systems and serve as quality training data for other machine learning models. The goal of this review is to contribute a concise consolidated review of techniques involving the DCGAN framework and biometric samples for the improvement of biometric recognition systems and to be used as a reference point for future work in cybersecurity.

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Section: Discussionmentioning

confidence: 99%

Section: Improving Biometric Systemsmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

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Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Jenkins,

Roy

2024

Discov Artif Intell

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“…It can compute a 128-d embedding that quantifies the face itself [6]. FaceNet is a face recognition algorithm that learns the mapping from faces to a position in multidimensional space where the distance between points directly corresponds to a measure of face similarity [7]. Next, the face recognition model will be trained by deep learning.…”

Section: How It Operates -The Operational Modulementioning

confidence: 99%

Discover Life: Self-Fabricated Smart Mirror using Microprocessor and OpenCV

Yong,

Phun

2023

J. Phys.: Conf. Ser.

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Nowadays, people are trying hard to live more efficiently and productively by saving more time for daily activities. Mirrors and smartphones are the equipment that we usually use for our daily life. People usually prepare themselves in front of the mirror and at the same time check their phones for updated news, weather forecasts, and schedules, to plan their daily routine, but doing both activities at the same time might cause huge distractions, resulting in more time consumption. The main objective of this project is to develop a Smart Mirror that can provide information and also able to unlock the profile with a face recognition system through a camera. Raspberry Pi is the processor of the Smart Mirror, Python was used to create the connection platform in OpenCV. IR Frame was added to develop the touch function of the mirror. The role of a two-way mirror is to provide transparency for the LCD panel allowing light to pass through the mirror and at the same time as a normal mirror from the other side. The result has proven that time can be saved up from 100-300 % with the help of Smart Mirror. The detection range of the face recognition is within 3 meters with 92 % of accuracy.

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“…The loss func on employed in the final layer is known as triplet loss. FaceNet consists of the aforemen oned c o m p o n e n t s , w h i c h w e w i l l e x a m i n e sequen ally [11].…”

Section: Choosing a Cnn Modelmentioning

confidence: 99%

Application of artificial intelligence to build a security control software system in local military units

Nguyen,

Duong

2023

HIUJS

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This paper introduces the application of artificial intelligence to build a security control software system in local military units. This software system uses state-of-the-art convolutional neural networks (CNN SOTA) for facial recognition by testing two of the best facial recognition models currently available: the FaceNet model and the VGGFace model. Through testing on the proposed hardware, the FaceNet model meets the accuracy and speed requirements for practical application. The software includes multiple identity management categories to ensure information security and monitor the access of soldiers and other individuals. Additionally, the software features access management functions for protected areas, allowing for audio and visual alerts to ensure safety and security in those areas. The software also enables users to set up connections with other devices for efficient data collection and processing. Simultaneously, it supports synchronized data connection to help users save time and effort in managing information. Moreover, the software includes user-friendly interfaces and customizable settings, ensuring ease of use and adaptability to the specific needs of each military unit. this software system provides a comprehensive and effective solution for ensuring security and monitoring access in local military units. By leveraging artificial intelligence, the system can adapt and improve over time, offering enhanced performance and capabilities to meet the evolving security needs of military organizations. The innovative approach presented in this paper has the potential to significantly improve the overall security and efficiency of local military units, contributing to the safety and well-being of both military personnel and the communities they serve.

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Towards an Effective Approach for Face Recognition with DCGANs Data Augmentation

Cited by 7 publications

References 20 publications

Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Discover Life: Self-Fabricated Smart Mirror using Microprocessor and OpenCV

Application of artificial intelligence to build a security control software system in local military units

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