Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection

Ma, Yukun; Xu, Yaowen; Liu, Fanghao

doi:10.1109/access.2020.2971224

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…Lastly, the DWT-LBP-DCT features are generated to illustrate frequency spatial temporal data of video. Yukun et al [ 115 ] designed a scheme where two perspective dynamic features are extracted where the former involves the temporal motion properties of face video and latter uses visual beats of noise pattern. These extracted features are fused at decision level and an SVM classifier is fitted to differentiate face images into two labels.…”

Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

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The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.

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Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

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“…To solve the problem, the adaptation layer is exploited to transfer data with diverse distributions [13], [14], [15]. Transfer learning recently has been successfully applied in age estimation [16] or face expression recognition [17], [18], [19] of the cross-database classification. Whereas cross-database based pneumonia detection for heterogeneous data is of more practical significance and is still not really involved.…”

Section: Introductionmentioning

confidence: 99%

A New Framework Based on Transfer Learning for Cross-Database Pneumonia Detection

Shan

Wen

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Cross-database classification means that the model is able to apply to the serious disequilibrium of data distributions, and it is trained by one database while tested by another database. Thus, cross-database pneumonia detection is a challenging task. In this paper, we proposed a new framework based on transfer learning for cross-database pneumonia detection. First, based on transfer learning, we fine-tune a backbone that pre-trained on non-medical data by using a small amount of pneumonia images, which improves the detection performance on homogeneous dataset. Then in order to make the fine-tuned model applicable to cross-database classification, the adaptation layer combined with a self-learning strategy is proposed to retrain the model. The adaptation layer is to make the heterogeneous data distributions approximate and the self-learning strategy helps to tweak the model by generating pseudo-labels. Experiments on three pneumonia databases show that our proposed model completes the cross-database detection of pneumonia and shows good performance.

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A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain Classification

Shan

Wen

et al. 2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection

Cited by 7 publications

References 41 publications

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

A New Framework Based on Transfer Learning for Cross-Database Pneumonia Detection

A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain Classification

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