Auflistung nach Schlagwort "Face recognition"
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- KonferenzbeitragDeDiM: De-identification using a diffusion model(BIOSIG 2022, 2022) Hidetsugu Uchida, Narishige Abe and Shigefumi YamadaAs a countermeasure against malicious authentication in a face recognition system using a face image obtained from SNS or the like, de-identification methods based on adversarial example have been studied. However, since adversarial example directly uses the gradient information of a face recognition model, it is highly dependent on the model, and a de-identification effect and image quality are difficult to achieve for an unknown recognition model. In this study, we propose a novel de-identification method based on a diffusion model, which has high generalizability to an unknown recognition model by applying minute changes to face shapes. Experiments using LFW showed that the proposed method has a higher de-identification effect for unknown models and better image quality than a conventional method using adversarial example.
- KonferenzbeitragThe Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Damer, Naser; Grebe, Jonas Henry; Chen, Cong; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, ArjanFace recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases.We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.
- KonferenzbeitragEffects of sample stretching in face recognition(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Hedberg, Mathias FredrikFace stretching is something that can occur intentionally and unintentionally when preparing a face sample for enrollment in a face recognition system. In this paper we assess what affects both horizontal and vertical stretching have on a face recognition algorithms. Basic closed-set identification tests revealed that holistic face recognition algorithms performed poorly compared to feature based recognition algorithms when classifying non-stretched samples against templates based on stretched samples.
- KonferenzbeitragFusion of Face Demorphing and Deep Face Representations for Differential Morphing Attack Detection(BIOSIG 2022, 2022) Shiqerukaj, Elidona; Rathgeb, Christian; Merkle, Johannes; Drozdowski, Pawel; Tams, BenjaminAlgorithm fusion is frequently employed to improve the accuracy of pattern recognition tasks. This particularly applies to biometrics including attack detection mechanisms. In this work, we apply a fusion of two differential morphing attack detection methods, i.e. Demorphing and Deep Face Representations. Experiments are performed in a cross-database scenario using high-quality face morphs along with realistic live captures. Obtained results reveal that a weighted sum-based score-level fusion of Demorphing and Deep Face Representations improves the morphing attack detection accuracy. With the proposed fusion, a detection equal error rate of 4.9% is achieved, compared to detection equal error rates of 5.6% and 5.8% of the best individual morphing attack detection methods, respectively.
- TextdokumentMulti-scale facial scanning via spatial LSTM for latent facial feature representation(BIOSIG 2017, 2017) Kim,Seong Tae; Choi,Yeoreum; Ro,Yong ManIn the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation of facial regions. Moreover, with facial regions of various scales, the complementary information of the multi-scale facial features is encoded. Experimental results on public database showed that the proposed method outperformed the conventional methods by improving the face recognition accuracy under illumination variation.
- KonferenzbeitragMy Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Neto, Pedro; Boutros, Fadi; Pinto, João Ribeiro; Saffari, Mohsen; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.
- KonferenzbeitragOn Brightness Agnostic Adversarial Examples Against Face Recognition Systems(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Singh, Inderjeet; Momiyama, Satoru; Kakizaki, Kazuya; Araki, ToshinoriThis paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.
- KonferenzbeitragOn Recognizing Occluded Faces in the Wild(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Erakın, Mustafa Ekrem; Demir, Uğur; Ekenel, Hazım KemalFacial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks. We propose two evaluation protocols for this dataset. Benchmark experiments on the dataset have shown that no matter how powerful the deep face representation models are, their performance degrades significantly when they are tested on real-world occluded faces. It is observed that the performance drop is far less when the models are tested on synthetically generated occluded faces. The ROF dataset and the associated evaluation protocols are publicly available at the following link https://github.com/ekremerakin/RealWorldOccludedFaces.