Face Recognition Systems Under Spoofing Attacks

Chingovska, Ivana; Erdoğmuş, Nesli; Anjos, André; Marcel, Sébastien

doi:10.1007/978-3-319-28501-6_8

Cited by 80 publications

(47 citation statements)

References 70 publications

(133 reference statements)

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“…In order to distinguish real face features from fake faces, face liveness detection is a commonly used countermeasure approach. It is aimed at detecting physiological life signs in an identity [1].…”

Section: Introductionmentioning

confidence: 99%

Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP)

Parveen

Ahmad²,

Abbas

et al. 2016

Computers

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Abstract:Face spoofing is considered to be one of the prominent threats to face recognition systems. However, in order to improve the security measures of such biometric systems against deliberate spoof attacks, liveness detection has received significant recent attention from researchers. For this purpose, analysis of facial skin texture properties becomes more popular because of its limited resource requirement and lower processing cost. The traditional method of skin analysis for liveness detection was to use Local Binary Pattern (LBP) and its variants. LBP descriptors are effective, but they may exhibit certain limitations in near uniform patterns. Thus, in this paper, we demonstrate the effectiveness of Local Ternary Pattern (LTP) as an alternative to LBP. In addition, we adopted Dynamic Local Ternary Pattern (DLTP), which eliminates the manual threshold setting in LTP by using Weber's law. The proposed method was tested rigorously on four facial spoof databases: three are public domain databases and the other is the Universiti Putra Malaysia (UPM) face spoof database, which was compiled through this study. The results obtained from the proposed DLTP texture descriptor attained optimum accuracy and clearly outperformed the reported LBP and LTP texture descriptors.

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Section: Introductionmentioning

confidence: 99%

Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP)

Parveen

Ahmad²,

Abbas

et al. 2016

Computers

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“…3DMAD [15] is the first publicly available 3D masks dataset, which is recorded using Microsoft Kinect sensor and consists of Depth and RGB modalities. Another multi-modal face PAD dataset is Msspoof [9], containing visible (VIS) and near-infrared (NIR) images of real accesses and printed spoofing attacks with ≤ 21 objects.…”

Section: Related Work 21 Datasetsmentioning

confidence: 99%

“…is the limited number of data modalities. Most of the current datasets only have one modal (e.g., RGB), and the existing available multi-modal datasets [15,9] are scarce, including no more than 21 subjects.…”

Section: Introductionmentioning

confidence: 99%

A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

Zhang

Wang

Liu

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

150

158

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Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face antispoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤ 170) and modalities (≤ 2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1, 000 subjects with 21, 000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https

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“…The use of fingerprint technology is well-known and has become highly integrated into certain modern devices [5]- [7]. Face recognition has also been explored for applications on consumer devices [8], [9] but has not been widely adopted in practice, due in part to concerns on the ease with which face data can be captured and used in spoofing attacks [10], [11].…”

Section: A Biometrics On Smartphonesmentioning

confidence: 99%

“…Thus, while palmprint has not seen the mainstream use of face, fingerprint and iris biometrics, it is an equally valid candidate for use in smartphones and has the advantage that no additional sensing capabilities are required. And there is a further advantage -one of the weaknesses of facial images [11] and fingerprints [6] lies in the ease with which high quality samples of these biometrics can be obtained by an attacker. Palmprints are not as easy to acquire without the user's consent as people do not easily leave behind palmprint copies or expose them during daily activities.…”

Section: B Palmprint As a Smartphones Biometricmentioning

confidence: 99%