Detecting Disguise Attacks on Multi-spectral Face Recognition Through Spectral Signatures

Ramachandra, Raghavendra; Vetrekar, Narayan; Raja, Kiran B.; Gad, Rajendra S.; Busch, Christoph

doi:10.1109/icpr.2018.8545076

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Liu and Kumar [24] investigated a multispectral approach to detect face masks, they used two sensors to acquire real and masks faces under visible and near infrared, and the results showed that near infrared based imaging 3D face masks offered superior performance compared to visible illumination. Raghavendra et al [25] presented a new approach based on using spectral signatures obtained from a spectral camera operating in eight narrow spectral bands across the visible and near infrared spectrum to detect fake faces. George et al [26] proposed a multi-channel CNN-based approach for attack detection, they captured data images under color, depth, near-infrared and thermal four different channels, and obtained better results with an ACER of 0.3% compared to feature-based approaches.…”

Section: Multi Spectrums Based Methodsmentioning

confidence: 99%

Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors

Zhang

et al. 2020

IEEE Access

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Under confrontational environment, individual can impersonate others by wearing masks or other skills to conceal real identity, which brings enormous challenges to physical identity recognition. Moreover, massive fake attributes seriously threaten identity management. Aiming at the existence of fake attributes during multimodal identification, we propose a novel method to detect fake attributes and compute real identity for security of systems. Most previous methods focused on the differences about features between fake and normal attributes, but each method generally targeted one type of fake attribute, and the results decreased a lot with unknown attacks. In this paper, we first explore the essential differences of data distribution caused by natural and human behaviors, then with order-of-consensus-calculation based on the differences, fake attributes are detected by analyzing the rank of consensus identity in recognition results, finally maximum-consensus-calculation is applied to compute real identity for evaluating detection performance. Experimental results on face, fingerprint, and voiceprint demonstrate that the proposed method can detect fake attributes effectively, which has a higher accuracy, and the accuracy of identity recognition is increased obviously by about 13.20% with forgery detection. The additional experiments further confirm the feasibility of the proposed method with increase of fake attributes. Furthermore, the proposed method can deal with different kinds of attacks, even with unknown attacks, and it is also significant to improve the security of identification with complex environment.INDEX TERMS Fake attributes, natural and human behaviors, forgery detection, identification.

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Section: Multi Spectrums Based Methodsmentioning

confidence: 99%

Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors

Zhang

et al. 2020

IEEE Access

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“…✓ ✓ ✓ Sober Drunk [38], [39] ✓ PUCV-DTF [54] ✓ TFW [137] ✓ ✓ SpeakingFaces [138] ✓ ✓ KTFE [41] ✓ ✓ NIST/Equinox [139] ✓ ✓ SDFD [55] ✓ CBSR-NIR [140] ✓ ✓ RWTH [141] ✓ UNCC-ThermalFace [60] ✓ IRIS-M3 [16] ✓ UWA-HSFD [142] ✓ an uncontrolled environment with high distortions resulting from changes in illumination. A nighttime situation is an example of a condition where human recognition, based exclusively on visible spectrum pictures, may be impractical.…”

Section: Thermal and Multispectral Scanningmentioning

confidence: 99%

“…Subsequently, several other data sets, including NFRAD-DB [29], LDHF-DB [36], ND-NIVL [44], ARL-MMFD1 [135], SDFD [55], and UNCC-ThermalFace [60] have been made accessible to the research community. Each of these data VOLUME 4, 2016 sets captures thermal imaging across different wave bands, encompasses multiple illumination variations, and introduces variability in facial expressions and poses.…”

Section: Thermal Infrared Imagingmentioning

confidence: 99%

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

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Due to their ease-of-use, biometric verification methods to control access to digital devices have become ubiquitous. Many rely on supervised machine learning, a process that is notoriously datahungry. At the same time, biometric data is sensitive from a privacy perspective, and a comprehensive review from a data set perspective is lacking. In this survey, we present a comprehensive review of multimodal face data sets (e.g., data sets containing RGB color plus other channels such as infrared or depth). This follows a trend in both industry and academia to use such additional modalities to improve the robustness and reliability of the resulting biometric verification systems. Furthermore, such data sets open the path to a plethora of additional applications, such as 3D face reconstruction (e.g., to create avatars for VR and AR environments), face detection, registration, alignment, and recognition systems, emotion detection, anti-spoofing, etc. We also provide information regarding the data acquisition setup and data attributes (ethnicities, poses, facial expressions, age, population size, etc.) as well as a thorough discussion of related applications and state-of-the-art benchmarking. Readers may thus use this survey as a tool to navigate the existing data sets both from the application and data set perspective. To existing surveys we contribute, to the best of our knowledge, the first exhaustive review of multimodalities in these data sets.

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“…The emphasis of the BRSU Spoof DB is towards the multispectral domain. The Spectral Disguise Face Database [26] is a collection of 54 male subjects with normal and disguised face images. The DB features images captured in the visible and near-infrared spectrums ranging from 530 nm to 1000 nm.…”

Section: Databases With Disguised Facial Imagesmentioning

confidence: 99%

Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm

et al. 2021

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Applications for facial recognition have eased the process of personal identification. However, there are increasing concerns about the performance of these systems against the challenges of presentation attacks, spoofing, and disguises. One of the reasons for the lack of a robustness of facial recognition algorithms in these challenges is the limited amount of suitable training data. This lack of training data can be addressed by creating a database with the subjects having several disguises, but this is an expensive process. Another approach is to use generative adversarial networks to synthesize facial images with the required disguise add-ons. In this paper, we present a synthetic disguised face database for the training and evaluation of robust facial recognition algorithms. Furthermore, we present a methodology for generating synthetic facial images for the desired disguise add-ons. Cycle-consistency loss is used to generate facial images with disguises, e.g., fake beards, makeup, and glasses, from normal face images. Additionally, an automated filtering scheme is presented for automated data filtering from the synthesized faces. Finally, facial recognition experiments are performed on the proposed synthetic data to show the efficacy of the proposed methodology and the presented database. Training on the proposed database achieves an improvement in the rank-1 recognition rate (68.3%), over a model trained on the original nondisguised face images.

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Detecting Disguise Attacks on Multi-spectral Face Recognition Through Spectral Signatures

Cited by 8 publications

References 12 publications

Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors

Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm

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