Face spoofing detection from single images using micro-texture analysis

doi:10.1109/IJCB.2011.6117510

Home
/
Papers
/
Face spoofing detection from single images using micro-texture analysis

Proceedings Article•DOI•

Face spoofing detection from single images using micro-texture analysis

Jukka Maatta¹, Abdenour Hadid¹, Matti Pietikäinen¹•Institutions (1)

University of Oulu¹

11 Oct 2011-International Journal of Central Banking (IEEE)-pp 1-7

TL;DR: This work presents a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print, and analyzes the texture of the facial images using multi-scale local binary patterns (LBP).

read less

Abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Face Spoof Detection With Image Distortion Analysis

[...]

Di Wen¹, Hu Han¹, Anil K. Jain¹•Institutions (1)

Michigan State University¹

04 Feb 2015-IEEE Transactions on Information Forensics and Security

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.

...read moreread less

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.

...read moreread less

716 citations

Proceedings Article•

On the effectiveness of local binary patterns in face anti-spoofing

[...]

Ivana Chingovska¹, André Anjos¹, Sébastien Marcel¹•Institutions (1)

Idiap Research Institute¹

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.

...read moreread less

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.

...read moreread less

707 citations

Cites background or methods from "Face spoofing detection from single..."

...Finally, we apply the studied counter-measure and the algorithm given in [7] to all the available databases....
[...]
...LBP 3×3 + χ(2) LBP 3×3 + LDA LBP 3×3 + SVM LBP [7] + SVM dev test dev test dev test dev test...
[...]
...uniform areas of the image, such as the forehead or the cheeks [7]....
[...]
...Most recently, the approach proposed in [7] exploits yet another texture feature, LBP....
[...]
...The feature vector which is used for spoofing detection in this work is a simple normalized histogram of LBP 3×3 codes, as opposed to the concatenation of more complex parametrized LBP proposed in [7]....
[...]

Proceedings Article•DOI•

A face antispoofing database with diverse attacks

[...]

Zhiwei Zhang¹, Junjie Yan¹, Sifei Liu¹, Zhen Lei¹, Dong Yi¹, Stan Z. Li¹ - Show less +2 more•Institutions (1)

Chinese Academy of Sciences¹

06 Aug 2012

TL;DR: A face antispoofing database which covers a diverse range of potential attack variations, and a baseline algorithm is given for comparison, which explores the high frequency information in the facial region to determine the liveness.

...read moreread less

Abstract: Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature.

...read moreread less

680 citations

Cites methods from "Face spoofing detection from single..."

...So in this paper we restrict our concern only on the unimodal face biometrics under visible illumination just as the NUAA [2] and Idiap [3] database do....
[...]

Proceedings Article•DOI•

Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

[...]

Yaojie Liu¹, Amin Jourabloo¹, Xiaoming Liu¹•Institutions (1)

Michigan State University¹

18 Jun 2018

TL;DR: This paper argues the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues, and introduces a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations.

...read moreread less

Abstract: Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues. A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision. The estimated depth and rPPG are fused to distinguish live vs. spoof faces. Further, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations. Experiments show that our model achieves the state-of-the-art results on both intra- and cross-database testing.

...read moreread less

502 citations

Cites methods from "Face spoofing detection from single..."

...Researchers start the texture-based anti-spoofing approaches by feeding handcrafted features to binary classifiers [13,18, 19,27,33,34,38,49]....
[...]
...Many prior works utilize hand-crafted features, such as LBP [18, 19, 33], HoG [27, 49], SIFT [38] and SURF [13], and adopt traditional classifiers such as SVM and LDA....
[...]

Journal Article•DOI•

Face Spoofing Detection Using Colour Texture Analysis

[...]

Zinelabidine Boulkenafet¹, Jukka Komulainen¹, Abdenour Hadid¹•Institutions (1)

University of Oulu¹

20 Apr 2016-IEEE Transactions on Information Forensics and Security

TL;DR: This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis that exploits the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces.

...read moreread less

Abstract: Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. More specifically, the feature histograms are computed over each image band separately. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts.

...read moreread less

449 citations

Cites background from "Face spoofing detection from single..."

...The most crude attack attempts performed, e.g. using small mobile phone displays or prints with strong artifacts, can be detected by analysing the texture and the quality of the captured gray-scale face images....
[...]
...The experimental results are then introduced and discussed in Section V. Finally, Section VI draws concluding remarks and points out possible future research directions....
[...]
...1) Local Binary Patterns (LBP): The LBP descriptor proposed by Ojala et al. [50] is a highly discriminative grayscale texture descriptor....
[...]
...…texture content using four other descriptors: the local phase quantization (LPQ), the co-occurrence of adjacent local binary patterns (CoALBP), the binarized statistical image features (BSIF) and the scaleinvariant descriptor (SID) that have already shown to be effective in gray-scale texture…...
[...]

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126

Collapse

References

PDF

Open Access

More filters

Journal Article•DOI•

LIBSVM: A library for support vector machines

[...]

Chih-Chung Chang¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

06 May 2011-ACM Transactions on Intelligent Systems and Technology

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

...read moreread less

Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

...read moreread less

40,826 citations

"Face spoofing detection from single..." refers methods in this paper

...LibSVM Library [3] is used for SVM implementation in all experiments....
[...]

Statistical learning theory

[...]

Vladimir Vapnik

01 Jan 1998

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

26,531 citations

"Face spoofing detection from single..." refers methods in this paper

...The vectors in the feature space are then fed to an SVM classifier which determines whether the micro-texture patterns characterize a live person or a fake image....
[...]
...Once the enhanced histograms are computed, we use a nonlinear SVM classifier with radial basis function kernel [13] for determining whether the input image corresponds to a live face or not....
[...]
...Our proposed approach, using multi-scale local binary patterns and SVM, is then described in Section 3 and evaluated in Section 4, where extensive experiments are conducted....
[...]
...The computed features are fed to SVM classifiers....
[...]
...LibSVM Library [3] is used for SVM implementation in all experiments....
[...]

Journal Article•DOI•

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

[...]

Timo Ojala¹, Matti Pietikäinen¹, Topi Mäenpää¹•Institutions (1)

University of Oulu¹

01 Jul 2002-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.

...read moreread less

Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

...read moreread less

14,245 citations

"Face spoofing detection from single..." refers background or methods in this paper

...Superscript u2 stands for using only uniform patterns and labeling all remaining patterns with a single label (see [9] for details)....
[...]
...The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP) [9] and encodes the micro-texture patterns into an enhanced feature histogram [1]....
[...]
...[9], is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local Figure 2....
[...]
...Our method adopts the local binary patterns [9], a powerful texture operator, for describing not only the microtextures but also their spatial information....
[...]

Journal Article•DOI•

Face Description with Local Binary Patterns: Application to Face Recognition

[...]

Timo Ahonen¹, Abdenour Hadid¹, Matti Pietikäinen¹•Institutions (1)

University of Oulu¹

01 Dec 2006-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.

...read moreread less

Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

...read moreread less

5,563 citations

"Face spoofing detection from single..." refers background or methods in this paper

...The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP) [9] and encodes the micro-texture patterns into an enhanced feature histogram [1]....
[...]
...Such a representation is shown to be very adequate for face recognition [1]....
[...]

Journal Article•DOI•

Texture features for browsing and retrieval of image data

[...]

B.S. Manjunath¹, Wei-Ying Ma¹•Institutions (1)

University of California, Santa Barbara¹

01 Aug 1996-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.

...read moreread less

Abstract: Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.

...read moreread less

4,017 citations

"Face spoofing detection from single..." refers methods in this paper

...We started by evaluating the performance of three powerful texture operators, namely LBP, Local Phase Quantization (LPQ) [10] and Gabor wavelets [7] in discriminating real faces from fake ones (i....
[...]