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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Image segmentation

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Performance evaluation for ranking-based discretisation

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Procedia computer science 2020年 176卷 3335-3344页

作者： Grzegorz Baron Urszula Stańczyk Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes’ values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes.

关键词： discretisation feature selection ranking importance of attributes stylometry classification

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Impact of shape on apparent translucency differences 27

Impact of shape on apparent translucency differences

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27th Color and Imaging Conference: Color science and Engineering Systems, Technologies, and Applications, CIC 2019

作者： Gigilashvili, Davit Urban, Philipp Thomas, Jean-Baptiste Hardeberg, Jon Yngve Pedersen, Marius Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

ISBN: (纸本)9780892083435

Translucency is one of the major appearance attributes. Apparent translucency is impacted by various factors including object shape and geometry. Despite general proposals that object shape and geometry have a significant effect on apparent translucency, no quantification has been made so far. Quantifying and modeling the impact of geometry, as well as comprehensive understanding of the translucency perception process, are a point of not only academic, but also industrial interest with 3D printing as an example among many. We hypothesize that a presence of thin areas in the object facilitates material translucency estimation and changes in material properties have larger impact on apparent translucency of the objects with thin areas. computergenerated images of objects with various geometry and thickness have been used for a psychophysical experiment in order to quantify apparent translucency difference between objects while varying material absorption and scattering properties. Finally, absorption and scattering difference thresholds where the human visual system starts perceiving translucency difference need to be identified and its consistency needs to be analyzed across different shapes and geometries. © 2019 Society for Imaging science and Technology.

关键词： Geometry

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Face Presentation Attack Detection in Ultraviolet Spectrum via Local and Global Features

Face Presentation Attack Detection in Ultraviolet Spectrum v...

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International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Dirk Siegmund Florian Kerckhoff Javier Yeste Magdaleno Nils Jansen Florian Kirchbuchner Arjan Kuijper Fraunhofer Institute for Computer Graphics Research (IGD) Darmstadt Germany Department of Computer Science Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

The security of the commonly used face recognition algorithms is often doubted, as they appear vulnerable to so-called presentation attacks. While there are a number of detection methods that are using different light spectra to detect these attacks this is the first work to explore skin properties using the ultraviolet spectrum. Our multi-sensor approach consists of learning features that appear in the comparison of two images, one in the visible and one in the ultraviolet spectrum. We use brightness and keypoints as features for training, experimenting with different learning strategies. We present the results of our evaluation on our novel Face UV PAD database. The results of our method are evaluated in an leave-one-out comparison, where we achieved an APCER/BPCER of 0%/0.2%. The results obtained indicate that UV images in presentation attack detection include useful information that are not easy to overcome.

关键词： Skin Faces Three-dimensional displays Databases Feature extraction Brightness Two dimensional displays

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On the assessment of face image quality based on handcrafted features

On the assessment of face image quality based on handcrafted...

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International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Olaf Henniger Biying Fu Cong Chen Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

This paper studies the assessment of the quality of face images, predicting the utility of face images for automated recognition. The utility of frontal face images from a publicly available dataset was assessed by comparing them with each other using commercial off-the-shelf face recognition systems. Multiple face image features delineating face symmetry and characteristics of the capture process were analysed to find features predictive of utility. The selected features were used to build system-specific and generic random forest classifiers.

关键词： Faces Training Face recognition Histograms Image quality Testing Forestry

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Neural human video rendering by learning dynamic textures and rendering-to-video translation

arXiv

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arXiv 2020年

作者： Liu, Lingjie Xu, Weipeng Habermann, Marc Zollhöfer, Michael Bernard, Florian Kim, Hyeongwoo Wang, Wenping Theobalt, Christian The Department of Computer Science The University of Hong Kong Hong Kong The Graphics Vision and Video Group Max Planck Institute for Informatics Saarbrücken66123 Germany The Department of Computer Science Computer Graphics Laboratory Stanford University CA94305 United States

Synthesizing realistic videos of humans using neural networks has been a popular alternative to the conventional graphics-based rendering pipeline due to its high efficiency. Existing works typically formulate this as an image-to-image translation problem in 2D screen space, which leads to artifacts such as over-smoothing, missing body parts, and temporal instability of fine-scale detail, such as pose-dependent wrinkles in the clothing. In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space. More specifically, our method relies on the combination of two convolutional neural networks (CNNs). Given the pose information, the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details, and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN. We demonstrate several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show significant improvement over the state of the art both qualitatively and quantitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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Beyond identity: what information is stored in biometric face templates?

arXiv

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arXiv 2020年

作者： Terhörst, Philipp Fährmann, Daniel Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt64283 Germany Department of Computer Science Technical University of Darmstadt Darmstadt64289 Germany

Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user’s privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates. Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

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Assessing quality of decision reducts

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Procedia computer science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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Reduct-based ranking of attributes

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Procedia computer science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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