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Proceedings ArticleDOI

Face anti-spoofing with multifeature videolet aggregation

TL;DR: A novel multi-feature evidence aggregation method for face spoofing detection that fuses evidence from features encoding of both texture and motion properties in the face and also the surrounding scene regions and provides robustness to different attacks.
Abstract: Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively.
Citations
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Proceedings ArticleDOI
01 Jun 2019
TL;DR: This paper designs a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF) and shows superior performance in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database.
Abstract: With the advancements in technology and growing popularity of facial photo editing in the social media landscape, tools such as face swapping and face morphing have become increasingly accessible to the general public. It opens up the possibilities for different kinds of face presentation attacks, which can be taken advantage of by impostors to gain unauthorized access of a biometric system. Moreover, the wide availability of 3D printers has caused a shift from print attacks to 3D mask attacks. With increasing types of attacks, it is necessary to come up with a generic and ubiquitous algorithm with a panoptic view of these attacks, and can detect a spoofed image irrespective of the method used. The key contribution of this paper is designing a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF). The performance is evaluated for digital and physical attacks in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database. Experimental results showcase the superior performance of the proposed presentation attack detection algorithm.

21 citations

Proceedings ArticleDOI
15 Oct 2019
TL;DR: A novel Multi-modal Multi-layer Fusion Convolutional Neural Network (mmfCNN), which targets at finding a discriminative model for recognizing the subtle differences between live and spoof faces, and utilizes a multi-layer fusion model to further aggregate the features from different layers.
Abstract: Face anti-spoofing detection is critical to guarantee the security of biometric face recognition systems. Despite extensive advances in facial anti-spoofing based on single-model image, little work has been devoted to multi-modal anti-spoofing, which is however widely encountered in real-world scenarios. Following the recent progress, this paper mainly focuses on multi-modal face anti-spoofing and aims to solve the following two challenges: (1) how to effectively fuse multi-modal information; and (2) how to effectively learn distinguishable features despite single cross-entropy loss. We propose a novel Multi-modal Multi-layer Fusion Convolutional Neural Network (mmfCNN), which targets at finding a discriminative model for recognizing the subtle differences between live and spoof faces. The mmfCNN can fully use different information provided by diverse modalities, which is based on a weight-adaptation aggregation approach. Specifically, we utilize a multi-layer fusion model to further aggregate the features from different layers, which fuses the low-, mid- and high-level information from different modalities in a unified framework. Moreover, a novel Average Binary Center (ABC) loss is proposed to maximize the dissimilarity between the features of live and spoof faces, which helps to stabilize the training to generate a robust and discriminative model. Extensive experiments conducted on the CISIA-SURF and 3DMAD datasets verify the significance and generalization capability of the proposed method for the face anti-spoofing task. Code is available at: https://github.com/SkyKuang/Face-anti-spoofing.

19 citations


Cites methods from "Face anti-spoofing with multifeatur..."

  • ...Our mmfCNN achieves the state-of-the-art performance compared with other methods [5, 9, 11, 23, 26, 36]....

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Book ChapterDOI
20 Jul 2022
TL;DR: In this paper , a generative domain adaptation (GDA) framework combines two carefully designed consistency constraints: 1) Inter-domain neural statistic consistency guides the generator in narrowing the inter-domain gap.
Abstract: Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have drawn growing attention due to promising performances for target scenarios. Most existing UDA FAS methods typically fit the trained models to the target domain via aligning the distribution of semantic high-level features. However, insufficient supervision of unlabeled target domains and neglect of low-level feature alignment degrade the performances of existing methods. To address these issues, we propose a novel perspective of UDA FAS that directly fits the target data to the models, i.e., stylizes the target data to the source-domain style via image translation, and further feeds the stylized data into the well-trained source model for classification. The proposed Generative Domain Adaptation (GDA) framework combines two carefully designed consistency constraints: 1) Inter-domain neural statistic consistency guides the generator in narrowing the inter-domain gap. 2) Dual-level semantic consistency ensures the semantic quality of stylized images. Besides, we propose intra-domain spectrum mixup to further expand target data distributions to ensure generalization and reduce the intra-domain gap. Extensive experiments and visualizations demonstrate the effectiveness of our method against the state-of-the-art methods.

18 citations

Journal ArticleDOI
TL;DR: This study investigates the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing, and proposes the use of a single deep network trained to detect both face and Iris attacks.
Abstract: Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.

17 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: For the first time, it is shown that simple intensity transforms such as Gamma correction, log transform, and brightness control can help an attacker to deceive face presentation attack detection algorithms.
Abstract: Presentation attacks can provide unauthorized access to the users and fool face recognition systems for both small scale and large scale applications. Among all the presentation attacks, 2D print and replay attacks are very popular due to their ease and cost-effectiveness in attacking face recognition systems. However, over the years, there are several successful presentation attack detection algorithms developed to detect 2D print and replay attacks. Generally, 2D presentation attacks are detected using the presence or absence of micro patterns which distinguish a real input from an attacked input. However, if a smart attacker digitally "pre-processes" the image using intensity transforms and then performs 2D presentation attack, differences between real and attacked samples due to the micro-patterns would be minimized. In this paper, for the first time, we show that simple intensity transforms such as Gamma correction, log transform, and brightness control can help an attacker to deceive face presentation attack detection algorithms. Experimental results demonstrate that the smart attacker can increase the error rate of the hand-crafted as well as deep learning based presentation attack detectors.

15 citations


Cites background from "Face anti-spoofing with multifeatur..."

  • ...The popular texture measure explored in literature are local binary patterns (LBP) [20, 21, 22] and its variants such as binarized statistical image features (BSIF) [23], Gabor features [24], Haralick features [25], Moiŕe patterns [26], and image quality [27]....

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References
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Journal ArticleDOI
TL;DR: A novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered and both the VLBP and LBP-TOP clearly outperformed the earlier approaches.
Abstract: Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation

2,653 citations


"Face anti-spoofing with multifeatur..." refers background in this paper

  • ...Dynamic texture features such as LBP-TOP [22] are studied in this regard....

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Journal ArticleDOI
TL;DR: The inherent strengths of biometrics-based authentication are outlined, the weak links in systems employing biometric authentication are identified, and new solutions for eliminating these weak links are presented.
Abstract: Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as e-commerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods.

1,709 citations


"Face anti-spoofing with multifeatur..." refers background in this paper

  • ...Biometric systems have different points of vulnerability such as sensor attacks, overriding feature extraction, tampering feature representation, corrupting matcher, tampering stored template, and overriding decision [18]....

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01 Jan 2009
TL;DR: This thesis builds a human-assisted motion annotation system to obtain ground-truth motion, missing in the literature, for natural video sequences, and proposes SIFT flow, a new framework for image parsing by transferring the metadata information from the images in a large database to an unknown query image.
Abstract: The focus of motion analysis has been on estimating a flow vector for every pixel by matching intensities. In my thesis, I will explore motion representations beyond the pixel level and new applications to which these representations lead. I first focus on analyzing motion from video sequences. Traditional motion analysis suffers from the inappropriate modeling of the grouping relationship of pixels and from a lack of ground-truth data. Using layers as the interface for humans to interact with videos, we build a human-assisted motion annotation system to obtain ground-truth motion, missing in the literature, for natural video sequences. Furthermore, we show that with the layer representation, we can detect and magnify small motions to make them visible to human eyes. Then we move to a contour presentation to analyze the motion for textureless objects under occlusion. We demonstrate that simultaneous boundary grouping and motion analysis can solve challenging data, where the traditional pixel-wise motion analysis fails. In the second part of my thesis, I will show the benefits of matching local image structures instead of intensity values. We propose SIFT flow that establishes dense, semantically meaningful correspondence between two images across scenes by matching pixel-wise SIFT features. Using SIFT flow, we develop a new framework for image parsing by transferring the metadata information, such as annotation, motion and depth, from the images in a large database to an unknown query image. We demonstrate this framework using new applications such as predicting motion from a single image and motion synthesis via object transfer. Based on SIFT flow, we introduce a nonparametric scene parsing system using label transfer, with very promising experimental results suggesting that our system outperforms state-of-the-art techniques based on training classifiers. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

899 citations


"Face anti-spoofing with multifeatur..." refers methods in this paper

  • ...The orientation based optical flow vector is computed by solving the optimization problem 1 using conjugate gradient method [12]....

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Journal ArticleDOI
TL;DR: An efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA) that outperforms the state-of-the-art methods in spoof detection and highlights the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.
Abstract: Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.

716 citations


"Face anti-spoofing with multifeatur..." refers background or methods in this paper

  • ...• On MSU dataset, HOOF obtains tremendous improvement in EER (from 30.41 to 2.50...

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  • ...Similarly, at the Inter Feature Fusion stage, the correlation of 0.51, 0.62, and 0.66 is observed for CASIA, MSU, and 3DMAD datasets, respectively....

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  • ...MSU dataset contains a higher fraction of replay attack videos compared to CASIA....

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  • ...• Performance of the Proposed Approach: The proposed fusion approach (using HOOF and multi-LBP with face and scene aggregated over videolets) provides 0% EER with uncontrolled illumination and background on both MSU and 3DMAD datasets....

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  • ...Orthogonal to the LBP texture descriptors based approaches, quality assessment metrics such as specular reflection, blurring and color density are also explored for anti-spoofing [10], [20]....

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Proceedings Article
27 Sep 2012
TL;DR: This paper inspects the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes and concludes that LBP show moderate discriminability when confronted with a wide set of attack types.
Abstract: Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.

707 citations


Additional excerpts

  • ...The face anti-spoofing problem is extensively studied in literature, particularly with the introduction of Print Attack dataset [1], Replay Attack dataset [5], CASIA-FASD spoofing dataset [21], 3DMAD database [7], and MSU mobile face spoofing database [20]....

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