77 research outputs found
Face Spoof Detection from Single Image Using Various Parameters
To detect duplication of identity during authentication of online payment on mobile or personal computer, the automatic face recognition is widely used now days. The biometric presentation attacks can be performed to gain access to these systems. It is performed by presenting the authorized person’s photo or video. Hence it is important to study the various face spoof attacks. Currently proposed face spoof detection techniques have less generalization ability as these are not considering all factors and do not detect the spoofing medium.The four features such as specular reflection, blurriness, chromatic moment and color diversity are used to analyze the image distortion. The different classifiers are trained for printed photo attack and video replay attack to differentiate between genuine and spoof faces. We also propose an approach to detect the spoofing medium by checking the boundary of the captured image during the photo attack and video attack and an approach to detect the blinking of eye for detecting liveness. It gives us high efficiency rather than existing methods
No intruders - securing face biometric systems from spoofing attacks
The use of face verification systems as a primary source of authentication has been very common over past few years. Better and more reliable face recognition system are coming into existence. But despite of the advance in face recognition systems, there are still many open breaches left in this domain. One of the practical challenge is to secure face biometric systems from intruder’s attacks, where an unauthorized person tries to gain access by showing the counterfeit evidence in front of face biometric system. The face-biometric system having only single 2-D camera is unaware that it is facing an attack by an unauthorized person. The idea here is to propose a solution which can be easily integrated to the existing systems without any additional hardware deployment. This field of detection of imposter attempts is still an open research problem, as more sophisticated and advanced spoofing attempts come into play.
In this thesis, the problem of securing the biometric systems from these unauthorized or spoofing attacks is addressed. Moreover, independent multi-view face detection framework is also proposed in this thesis. We proposed three different counter-measures which can detect these imposter attempts and can be easily integrated into existing systems. The proposed solutions can run parallel with face recognition module. Mainly, these counter-measures are proposed to encounter the digital photo, printed photo and dynamic videos attacks. To exploit the characteristics of these attacks, we used a large set of features in the proposed solutions, namely local binary patterns, gray-level co-occurrence matrix, Gabor wavelet features, space-time autocorrelation of gradients, image quality based features. We further performed extensive evaluations of these approaches on two different datasets. Support Vector Machine (SVM) with the linear kernel and Partial Least Square Regression (PLS) are used as the classifier for classification. The experimental results improve the current state-of-the-art reference techniques under the same attach categories
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection
Face Presentation Attack Detection (PAD) plays a pivotal role in securing
face recognition systems against spoofing attacks. Although great progress has
been made in designing face PAD methods, developing a model that can generalize
well to unseen test domains remains a significant challenge. Moreover, due to
different types of spoofing attacks, creating a dataset with a sufficient
number of samples for training deep neural networks is a laborious task. This
work proposes a comprehensive solution that combines synthetic data generation
and deep ensemble learning to enhance the generalization capabilities of face
PAD. Specifically, synthetic data is generated by blending a static image with
spatiotemporal encoded images using alpha composition and video distillation.
This way, we simulate motion blur with varying alpha values, thereby generating
diverse subsets of synthetic data that contribute to a more enriched training
set. Furthermore, multiple base models are trained on each subset of synthetic
data using stacked ensemble learning. This allows the models to learn
complementary features and representations from different synthetic subsets.
The meta-features generated by the base models are used as input to a new model
called the meta-model. The latter combines the predictions from the base
models, leveraging their complementary information to better handle unseen
target domains and enhance the overall performance. Experimental results on
four datasets demonstrate low half total error rates (HTERs) on three benchmark
datasets: CASIA-MFSD (8.92%), MSU-MFSD (4.81%), and OULU-NPU (6.70%). The
approach shows potential for advancing presentation attack detection by
utilizing large-scale synthetic data and the meta-model
Facial Spoofing Detection Using Temporal Texture Co-occurrence
Biometric person recognition systems based on facial images are increasingly used in a wide range of applications. However, the potential for face spoofing attacks remains a significant challenge to the security of such systems and finding better means of detecting such presentation attacks has become a necessity. In this paper, we propose a new spoofing detection method, which is based on temporal changes in texture information. A novel temporal texture descriptor is proposed to characterise the pattern of change in a short video sequence named Temporal Co-occurrence Adjacent Local Binary Pattern (TCoALBP). Experimental results using the CASIA-FA, Replay Attack and MSU-MFSD datasets; the proposed method shows the effectiveness of the proposed technique on these challenging datasets
- …