A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Chen, Rui; Li, Fēi; Tong, Ying; Wu, Minghu; Jiao, Yang

doi:10.1049/cit2.12090

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…RetinaFace is a more advanced face detection algorithm that provides precise bounding box localization. RetinaFace is computationally intelligent due to its ability to detect faces with high accuracy, even in challenging conditions such as occlusions, different poses, and varying lighting conditions [ 22 , 24 ]. RetinaFace achieves this using a multi-task learning approach, where it simultaneously predicts facial landmarks, face attributes, and face-bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

Computationally intelligent real-time security surveillance system in the education sector using deep learning

Abid,

Mahmood,

Ashraf

et al. 2024

PLoS ONE

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Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques employ template matching and geometric facial features for detecting faces, striving for a balance between detection time and accuracy. To address this issue, the current research presents an enhanced FaceNet network. The RetinaFace is employed to perform expeditious face detection and alignment. Subsequently, FaceNet, with an improved loss function is used to achieve face verification and recognition with high accuracy. The presented work involves a comparative evaluation of the proposed network framework against both traditional and deep learning techniques in terms of face detection and recognition performance. The experimental findings demonstrate that an enhanced FaceNet can successfully meet the real-time facial recognition requirements, and the accuracy of face recognition is 99.86% which fulfills the actual requirement. Consequently, the proposed solution holds significant potential for applications in face detection and recognition within the education sector for real-time security surveillance.

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Section: Introductionmentioning

confidence: 99%

Computationally intelligent real-time security surveillance system in the education sector using deep learning

Abid,

Mahmood,

Ashraf

et al. 2024

PLoS ONE

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A dual benchmarking study of facial forgery and facial forensics

Pham,

Huynh,

Nguyen

et al. 2024

CAAI Trans on Intel Tech

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In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. However, there is no comprehensive, fair, and unified performance evaluation to enlighten the community on best performing methods. The authors present a systematic benchmark beyond traditional surveys that provides in‐depth insights into facial forgery and facial forensics, grounding on robustness tests such as contrast, brightness, noise, resolution, missing information, and compression. The authors also provide a practical guideline of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never‐ending war between measures and countermeasures. The authors’ source code is open to the public.

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