Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community

The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database

The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database extends the previous Wide Multi-Channel Attack database(WMCA), with more channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra), and also a wide variety of attacks

Relay selection in FSO systems with all-optical relaying over Gamma-Gamma turbulence channels

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Bu bildiride, tüm-optiksel çoklu-röle kablosuz optik haberleşme sistemleri için Gamma-Gamma atmosferik türbülans kanalı varsayımı altında servis dışı kalma başarımı incelenmiştir. Röleler arası katı senkronizasyon ihtiyacından kurtulmak adına, röle seçim tekniginden faydalanılmış ve röleleme aşamasında sadece seçilen bir röle kullanılmıştır. Başarım sonuçları, röle seçiminin tüm rölelerin aktif oldugu işbirliği protokolüne göre belirgin seviyede iyileştirme sağladığını göstermektedir.TÜBİTA

Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features

Makeup is a simple and easy instrument that can alter the appearance of a person’s face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their close resemblance with genuine (non-makeup) appearances. Makeups can also degrade the performance of recognition systems and of various algorithms that use human face as an input. The detection of facial makeups is an effective prohibitory measure to minimize these problems. This work proposes a deep learning-based presentation attack detection (PAD) method to identify facial makeups. We propose the use of a convolutional neural network (CNN) to extract features that can distinguish between presentations with age-induced facial makeups (attacks), and those without makeup (bona-fide). These feature descriptors, based on shape and texture cues, are constructed from multiple intermediate layers of a CNN. We introduce a new dataset AIM (Age Induced Makeups) consisting of 200+ video presentations of old-age makeups and bona-fide, each. Our experiments indicate makeups in AIM result in 14% decrease in the median matching scores of a recent CNN-based FR system. We demonstrate accuracy of the proposed PAD method where 93% presentations in the AIM dataset are correctly classified. In additional testing, it also outperforms existing methods of detection of generic makeups. A simple score-level fusion, performed on the classification scores of shape- and texture-based features, can further improve the accuracy of the proposed makeup detector

Outage performance of MIMO free-space optical systems in gamma-gamma fading channels

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, we consider a MIMO Free-Space Optical (FSO) communication system over gamma-gamma turbulence channels and derive a closed-form analytical expression for the outage probability taking into account the effects of both the inner scale size of optical channel and the aperture averaging. Our results demonstrate that by increasing the number of transmit and/or receive apertures, the degrading effect of the inner scale size is effectively reduced by extracting the spatial diversity gain of the MIMO scheme. We further provide Monte Carlo simulation results to confirm the validity of the derived expression.European Commission ; TUB

The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database

The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database extends the previous Wide Multi-Channel Attack database(WMCA) \citegeorge_mccnn_tifs2019, with more channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra), and also a wide variety of attacks

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low \bona classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community

Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network

Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such as silicone masks. As the quality of presentation attack instruments improves over time, achieving reliable PA detection with visual spectra alone remains very challenging. We argue that analysis in multiple channels might help to address this issue. In this context, we propose a multi-channel Convolutional Neural Network based approach for presentation attack detection (PAD). We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks. Data from different channels such as color, depth, near-infrared and thermal are available to advance the research in face PAD. The proposed method was compared with feature-based approaches and found to outperform the baselines achieving an ACER of 0.3% on the introduced dataset. The database and the software to reproduce the results are made available publicly

On the relationship between speech-based breathing signal prediction evaluation measures and breathing parameters estimation

The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's pathological condition. More recently, neural network-based methods have started emerging for predicting the breathing signal from the speech signal. The neural networks are trained and evaluated with different objective measures, such as mean squared error (MSE) and Pearson's correlation. This paper investigates whether there is a systematic relationship between the different objective measures used for training and evaluating the neural network models and the end-goal, i.e. estimation of breathing parameters such as, breathing rate and tidal volume. Our investigations on two different data sets with two different neural network-based approaches show that there is no clear systematic relationship. In other words, obtaining a high Pearson's correlation on the evaluation set does not necessarily mean better breathing parameter estimation. Thus, indicating the need for developing other objective evaluation measures.</p