Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

Sharifi, Omid

doi:10.5815/ijigsp.2019.02.02

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Finally, these histogram feature descriptors are utilised to improve the texture information expression, which enhances the recognition effect of the network against face representation attacks. Sharifi [16] adopted the VGG16 network and the overlapped histograms of local binary pattern (OVLBP) method to gather facial information, used the score combination of VGG16 and OVLBP to form a fusion score vector, and finally employed the majority voting method to distinguish live and spoofed faces. Rehmana et al [17] combined deep features and handcrafted features to embed RGB data and corresponding handcrafted features into an architecture, which can improve the model's ability to recognise face representation attacks.…”

Section: Hybrid Methods Combining Handcrafted Features and Deep Featuresmentioning

confidence: 99%

Representation constraint‐based dual‐channel network for face antispoofing

Li,

Cui,

Wang

et al. 2023

IET Computer Vision

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Although multimodal face data have obvious advantages in describing live and spoofed features, single‐modality face antispoofing technologies are still widely used when it is difficult to obtain multimodal face images or inconvenient to integrate and deploy multimodal sensors. Since the live/spoofed representations in visible light facial images include considerable face identity information interference, existing deep learning‐based face antispoofing models achieve poor performance when only the visible light modality is used. To address the above problems, the authors design a dual‐channel network structure and a constrained representation learning method for face antispoofing. First, they design a dual‐channel attention mechanism‐based grouped convolutional neural network (CNN) to learn important deceptive cues in live and spoofed faces. Second, they design inner contrastive estimation‐based representation constraints for both live and spoofed samples to minimise the sample similarity loss to prevent the CNN from learning more facial appearance information. This increases the distance between live and spoofed faces and enhances the network's ability to identify deceptive cues. The evaluation results indicate that the framework we designed achieves an average classification error rate (ACER) of 2.37% on the visible light modality subset of the CASIA‐SURF dataset and an ACER of 2.4% on the CASIA‐SURF CeFA dataset, outperforming existing methods. The proposed method achieves low ACER scores in cross‐dataset testing, demonstrating its advantage in domain generalisation.

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Section: Hybrid Methods Combining Handcrafted Features and Deep Featuresmentioning

confidence: 99%

Representation constraint‐based dual‐channel network for face antispoofing

Li,

Cui,

Wang

et al. 2023

IET Computer Vision

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“…7(c)) fuses them for more generic representation. To make more reliable predictions, Sharifi [120] proposes to fuse the predicted scores from both handcrafted LBP features and deep VGG16 model. However, it is challenging to determine the optimal score weights for these two kinds of features.…”

Section: Hybrid (Handcraft + Deep Learning) Methodsmentioning

confidence: 99%

Deep Learning for Face Anti-Spoofing: A Survey

Qin

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.

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“…Li and Feng [47] used SVM to classify between real and fake by extracting handcrafted deep partial features from the convolutional responses. Two sets of feature information extracted out of the CNN model and OVLBP (overlapped histograms of local binary patterns) are fused by Sharifi [36] to form a score vector. A majority voting of CNN, OVLBP, and fused score helps in fake detection.…”

Section: Software-based Techniquesmentioning

confidence: 99%

Antispoofing in face biometrics: a comprehensive study on software-based techniques

Vinutha¹,

Thippeswamy²

2023

Comput. Sci. Inf. Technol.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

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Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

Cited by 7 publications

References 32 publications

Representation constraint‐based dual‐channel network for face antispoofing

Representation constraint‐based dual‐channel network for face antispoofing

Deep Learning for Face Anti-Spoofing: A Survey

Antispoofing in face biometrics: a comprehensive study on software-based techniques

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