Time Analysis of Pulse-Based Face Anti-Spoofing in Visible and NIR

Hernandez-Ortega, Javier; Fiérrez, Julián; Morales, Aythami; Tomé, Pedro

doi:10.1109/cvprw.2018.00096

Cited by 57 publications

(41 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, some methods explore liveness cues to detect 3D face presentation attacks, such as thermal signatures [8], gaze information [3,5,4], and pulse or heartbeat signals [25,26,16,22]. Based on the intrinsic liveness signals, these methods achieve an outstanding performance in distinguishing real faces from masks.…”

Section: D Face Pad Methodsmentioning

confidence: 99%

A Database for Face Presentation Attack Using Wax Figure Faces

Jia

Guo

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

View full text Add to dashboard Cite

Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small data size or poor authenticity due to the production difficulty and high cost. In this work, we introduce the first wax figure face database, WFFD, as one type of super-realistic 3D presentation attacks to spoof the FRS. This database consists of 2200 images with both real and wax figure faces (totally 4400 faces) with a high diversity from online collections. Experiments on this database first investigate the vulnerability of three popular FRS to this kind of new attack. Further, we evaluate the performance of several face presentation attack detection methods to show the attack abilities of this super-realistic face spoofing database.

show abstract

Section: D Face Pad Methodsmentioning

confidence: 99%

A Database for Face Presentation Attack Using Wax Figure Faces

Jia

Guo

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

View full text Add to dashboard Cite

show abstract

“…Nowara et al [49] compare PPGs of face and background to decide whether the face is live or not. Heusch et al [32] use the long-term statistical spectral on the pulse signals and Hernandez-Ortega et al [31] employ the near infrared against realistic artifacts. Moreover, combining with DNNs, Liu et al [42] extract spatial and temporal auxiliary information, e.g., depth map and rPPG signal, to distinguish whether it is a live face or spoofing face.…”

Section: Remote Photoplethysmography (Rppg)mentioning

confidence: 99%

DeepRhythm

Hua

Guo

Juefei-Xu

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

131

View full text Add to dashboard Cite

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake detection technique that exposes Deep-Fakes by monitoring the heartbeat rhythms. DeepRhythm utilizes dual-spatial-temporal attention to adapt to dynamically changing face and fake types. Extensive experiments on FaceForensics++ and DFDC-preview datasets have confirmed our conjecture and demonstrated not only the effectiveness, but also the generalization capability of DeepRhythm over different datasets by various DeepFakes generation techniques and multifarious challenging degradations. CCS CONCEPTS • Computing methodologies → Computer vision; • Security and privacy → Social aspects of security and privacy.

show abstract

“…The patterns of PSD present the discrimination of the eyewhether it is a real or a spoofed one. An alive eye has an obvious peak in the PSD, while a spoofed eye has random noise peaks in PSD [21].…”

Section: Extracting the Heart Pulsementioning

confidence: 99%

“…In the feature extraction stage, PSD is visible and its maximum power response is specified as first feature P. The frequency range (expected between 0.6 -4Hz), is the second feature R which is the ratio of P to the total power. The pair of features (P, R) are the outcome of three channels of RGB video [21].…”

Section: Extracting the Heart Pulsementioning

confidence: 99%

Composition of Methods to Ensure Iris Liveness and Authenticity

Al-Rashid¹

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

In a biometric system technology, a person is authenticated based on processing the unique features of the human biometric signs. One of the well known biometric systems is iris recognition, this technique being considered as one of the most secure authentication solutions in the biometric field. However, several attacks do exist that are able to spoof iris. In this paper, we propose a novel approach for securing the iris recognition system by eye liveness detection technique. The proposed system detects the eye liveness, and recognises the iris. This process includes multiple steps. As per the first step, the person opens his eye and the system reads remotely the changes in pupil size as a result of the response to the ambient illumination. Then, the system starts matching the iris with a database. The second step: the person closes his eye and the system remotely detects the heartbeats signals under the skin of the eyelid. As per the third step, the person opens his eyes again and the system reads the pupil size again and compares the results of the pupil size, and then the system matches again the iris with the above database. For the iris recognition to be validated, all the above checks have to be passed. We have conducted several experiments with our proposed system, based on a brand new dataset comprised of 40 subjects. In addition, we also used public datasets: CASIA-Interval, CASIA-Twin and Ubiris.V1. The achieved results show the quality and viability of our proposal.

show abstract

Time Analysis of Pulse-Based Face Anti-Spoofing in Visible and NIR

Cited by 57 publications

References 24 publications

A Database for Face Presentation Attack Using Wax Figure Faces

A Database for Face Presentation Attack Using Wax Figure Faces

DeepRhythm

Composition of Methods to Ensure Iris Liveness and Authenticity

Contact Info

Product

Resources

About