Applying CNN with Extracted Facial Patches using 3 Modalities to Detect 3D Face Spoof

Ewald, K. E.; Zeng, Liaoyuan; Zhengyao,; Mawuli, Cobbinah Bernard; Abubakar, Hassan Sani; Victor, Agbesi

doi:10.1109/iccwamtip51612.2020.9317329

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…The combination of different fusion methods further enhances the overall success of the post-fusion CDIT stage. Comparatively, this study evaluated two algorithms: RDWT-Haralick-SVM, based on an SVM classifier [27], and the MC-CNN algorithm using an artificial neural network for feature extraction [28]. Reference [5] showcased the RDWT-Haralick-SVM algorithm achieving an ACER of 3.44%, while the MC-CNN method obtained 0.3% ACER using the CDIT metric.…”

Section: Resultsmentioning

confidence: 99%

Face Anti-Spoofing Detection with Multi-Modal CNN Enhanced by ResNet

Shaker,

Al-Darraji

2024

J. Basrah Res. (Sci.)

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The growing prevalence of face recognition technology in various applications, including mobile devices, access control, and financial transactions, highlights its importance. However, the vulnerability of face recognition systems to attacks has been demonstrated, underscoring the necessity of addressing potential weaknesses that attackers may exploit. The paper delves into face presentation attack detection (PAD) within biometric systems, which is crucial for ensuring the reliability and security of face recognition algorithms. To address this issue, the paper proposes a method for face presentation attack detection using ResNet-50 in conjunction with multi-modal data, incorporating RGB, depth, infrared (IR), and thermal channels. The method explores diverse strategies to combine results from each modality, investigating various fusion techniques such as majority voting, weighted voting, average pooling, and a stacking classifier. The system has been tested on the WMCA dataset. It exhibits strong performance compared to existing methods, notably achieving an impressive ACER ratio of 0.087% with the stacking classifier. This approach proves effective by consolidating multiple modalities without requiring individual scenario-specific models, indicating promise for real-world applications

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Section: Resultsmentioning

confidence: 99%

Face Anti-Spoofing Detection with Multi-Modal CNN Enhanced by ResNet

Shaker,

Al-Darraji

2024

J. Basrah Res. (Sci.)

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“…The inclusion of the F1 Score, a metric combining precision and recall, ensures a balanced evaluation that considers both false positives and false negatives [27]. [28], [29]. Different layers were employed based on the system's operational principles [30]- [32].…”

Section: Methodsmentioning

confidence: 99%

“…The workflow of the system execution Figure 2 depicts the flow diagram of the system utilized in this research. The figure illustrates that an individual's webcam feed was analyzed through the YOLO and CNN algorithmic systems[28],[29]. Different layers were employed based on the system's operational principles [30]-[32].…”

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confidence: 99%

YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Dewanto,

Farid,

Rafdi Syah

et al. 2024

SJI

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Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.

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“…We focus on the two methods that demonstrated the best results in the paper [Geo19]. The first one is RDWT-Haralick-SVM method proposed in [Ewa20]. This method is based on RDWT-Haralick features and linear SVM classification.…”

Section: Baseline Methodsmentioning

confidence: 99%

An improved simple feature set for face presentation attack detection

Denisova¹

2022

CSRN

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Presentation attacks are weak points of facial biometrical authentication systems. Although several presentation attack detection methods were developed, the best of them require a sufficient amount of training data and rely on computationally intensive deep learning based features. Thus, most of them have difficulties with adaptation to new types of presentation attacks or new cameras. In this paper, we introduce a method for face presentation attack detection with low requirements for training data and high efficiency for a wide range of spoofing attacks. The method includes feature extraction and binary classification stages. We use a combination of simple statistical and texture features and describe the experimental results of feature adjustment and selection. We validate the proposed method using WMCA dataset. The experiments showed that the proposed features decrease the average classification error in comparison with the RDWT-Haralick-SVM method and demonstrate the best performance among non-CNN-based methods.

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Applying CNN with Extracted Facial Patches using 3 Modalities to Detect 3D Face Spoof

Cited by 6 publications

References 13 publications

Face Anti-Spoofing Detection with Multi-Modal CNN Enhanced by ResNet

Face Anti-Spoofing Detection with Multi-Modal CNN Enhanced by ResNet

YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

An improved simple feature set for face presentation attack detection

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