Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

Liu, Ajian; Li, Xuan; Wan, Jun; Escalera, Sérgio; Madadi, Meysam; Jin, Yi; Wu, Zhuoyuan; Yu, Xiaogang; Tan, Zichang; Yuan, Qi; Rui-kun, Yang; Zhou, Benjia; Guo, Guodong; Li, Stan Z.

doi:10.48550/arxiv.2004.10998

Cited by 2 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use accuracy to evaluate the face recognition performance. Accuracy [21] is defined as the ratio of the number which correctly matched samples to the total number of samples. A higher accuracy indicates better classifier performance.…”

Section: ) Evaluation Indicatorsmentioning

confidence: 99%

A Novel High-Performance Face Anti-Spoofing Detection Method

Qi,

Han,

Shi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Accurate face recognition technology is of great significance for face anti-counterfeiting. Due to illumination, posture, angle, and other reasons, the existing face liveness detection technology is difficult to adapt the environmental changes, resulting in low detection accuracy. To address this issue, this paper presents a novel high-performance face anti-spoofing detection method named RGCS_ConvNeXt. The data-enhanced face images are fed into the ConvNext network, which group convolution is added to extract the correlation between different features, and the coordinate attention mechanism is used to enhance the facial feature extraction capability both spatially and directionally. Then SPPF is used to extract the features at different scales to enhance the representation of the feature map. Finally, the facial key point detection technique is utilized to calculate the eye EAR value to achieve accurate face anti-counterfeiting recognition. The proposed algorithm shows an average classification error rate of 0.3%, 1.7%, 1.9%±1.5% and 2.8%±3.4%, respectively, on the four protocols of the OULU-NPU public dataset. On the Siw dataset, the average classification error rate is 0.69%, a reduction of 0.02% compared to the MA-Net network. The half-error rate on the MSU-MFSD dataset is 2.39%, a 0.21% reduction compared to the DPCNN network.The algorithm shows good accuracy on the OULU-NPU, MSU-MFSD and Siw datasets, reaching 99.64%, 98.40% and 99.25% respectively, 0.26% higher than the SE-FeatherNet network's average accuracy.

show abstract

Section: ) Evaluation Indicatorsmentioning

confidence: 99%

A Novel High-Performance Face Anti-Spoofing Detection Method

Qi,

Han,

Shi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Presentation attack detection: an analysis of spoofing in the wild (SiW) dataset using deep learning models

Thapa,

Chaudhari,

Roy

2023

Discov Artif Intell

View full text Add to dashboard Cite

Presentation attacks are executed to attain illegitimate access to the system. They are categorized by their mode of action as a print attack, replay attack, and spoof attack, and by their media of action as iris, biometrics, fingerprint, and face. Though there has been a rise in computational algorithms and models to detect presentation attack, generalization across different datasets remain an essential aspect of performance measure. In this paper, we present presentation attack detection (PAD) and presentation attack types of classification (PATC) models based on convolutional neural networks (CNN). We utilize the different attacks presented on the Spoofing in the wild (SiW) dataset to build these models. The PAD-CNN model is developed with a minimal footprint to optimize training time. High-performing models such as Mobilenet and Inceptionv3 are also used in this research work. In this study, we perform an independent test on images extracted from videos of both seen and unseen subjects. Overall, PAD-CNN performed better or on par with Mobilenet and Inceptionv3, even with less training time. Furthermore, these models were also trained to classify the types of presentation attacks with good results. The benchmarking of these models were done on two different datasets, NUAA photo imposter database and Replay-attack database utilizing transfer learning. Together, these results suggest the robustness and effectiveness of the proposed presentation attack detection models based on CNN on the SiW dataset.

show abstract

Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

Cited by 2 publications

References 35 publications

A Novel High-Performance Face Anti-Spoofing Detection Method

A Novel High-Performance Face Anti-Spoofing Detection Method

Presentation attack detection: an analysis of spoofing in the wild (SiW) dataset using deep learning models

Contact Info

Product

Resources

About