420 research outputs found
Impact of Face Image Quality Estimation on Presentation Attack Detection
Non-referential face image quality assessment methods have gained popularity
as a pre-filtering step on face recognition systems. In most of them, the
quality score is usually designed with face matching in mind. However, a small
amount of work has been done on measuring their impact and usefulness on
Presentation Attack Detection (PAD). In this paper, we study the effect of
quality assessment methods on filtering bona fide and attack samples, their
impact on PAD systems, and how the performance of such systems is improved when
training on a filtered (by quality) dataset. On a Vision Transformer PAD
algorithm, a reduction of 20% of the training dataset by removing lower quality
samples allowed us to improve the BPCER by 3% in a cross-dataset test
Face Liveness Detection under Processed Image Attacks
Face recognition is a mature and reliable technology for identifying people. Due
to high-definition cameras and supporting devices, it is considered the fastest and
the least intrusive biometric recognition modality. Nevertheless, effective spoofing
attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are
commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of
the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques
Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective
The liveness detection ability is explicitly required for current personal verification systems in many security applications. As a matter of fact, the project of any biometric verification system cannot ignore the vulnerability to spoofing or presentation attacks (PAs), which must be addressed by effective countermeasures from the beginning of the design process. However, despite significant improvements, especially by adopting deep learning approaches to fingerprint Presentation Attack Detectors (PADs), current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modelling the cause-effect relationships when two systems (spoof detection and matching) with non-zero error rates are integrated.
To solve this lack of investigations in the literature, we present in this PhD thesis a novel performance simulation model based on the probabilistic relationships between the Receiver Operating Characteristics (ROC) of the two systems when implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the editions of LivDet 2017-2019, the NIST Bozorth3, and the top-level VeriFinger 12.0 matchers. With the help of this simulator, the overall system performance can be predicted before actual implementation, thus simplifying the process of setting the best trade-off among error rates.
In the second part of this thesis, we exploit this model to define a practical evaluation criterion to assess whether operational points of the PAD exist that do not alter the expected or previous performance given by the verification system alone. Experimental simulations coupled with the theoretical expectations confirm that this trade-off allows a complete view of the sequential embedding potentials worthy of being extended to other integration approaches
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