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Photo privacy detection based on text classification and face clustering 6

Photo privacy detection based on text classification and fac...

引用

6th International Conference Information Technology and Nanotechnology. Session Image Processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Kopeykina, Lyudmila Savchenko, Andrey National Research University Higher School of Economics Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

Nowadays, the photo privacy detection is becoming an acute task due to a wide spread of mobile devices with photos published on social networks. As a photo might contain private or sensitive data, there is an urgent need to accurately determine them and impose restrictions on their processing. In this paper we focus on the task of personal data detection in a photo gallery. A novel two-stage approach is proposed. At first, text of scanned documents is processed based on an EAST text detector, and extracted text is recognized using Tesseract and neural network classifier. At the second stage, face clustering is implemented for the remaining photos to identify large groups of people (friends, relatives) whose photos also refer to personal data and must be processed directly on a mobile device. The remaining images can be sent to a remote server for processing with higher accuracy. The experimental results of text recognition and face clustering methods using various convolutional networks for facial features extraction are presented. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Classification (of information)

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The video-based age and gender recognition with convolution neural networks 7th

The video-based age and gender recognition with convolution ...

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7th International Conference on network analysis, NET 2017

作者： Kharchevnikova, Angelina S. Savchenko, Andrey V. National Research University Higher School of Economics Nizhniy Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783319962467

The paper reviews the problem of age and gender recognition methods for video data using modern deep convolutional neural networks. We present the comparative analysis of classifier fusion algorithms to aggregate decisions for individual frames. We implemented the video-based recognition system with several aggregation methods to improve the age and gender identification accuracy. The experimental comparison of the proposed approach with traditional simple voting using IJB-A, Indian Movies, and Kinect datasets is provided. It is demonstrated that themost accurate decisions are obtained using the geometricmean and mathematical expectation of the outputs at softmax layers of the convolutional neural networks for gender recognition and age prediction, respectively. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Convolution

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EmotiEffNet and Temporal Convolutional networks in Video-based Facial Expression Recognition and Action Unit Detection

EmotiEffNet and Temporal Convolutional Networks in Video-bas...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Andrey V. Savchenko Anna P. Sidorova Sber AI Lab Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia HSE University Nizhny Novgorod Russia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper examines the video-based facial expression recognition and action unit detection tasks. We propose to use pre-trained EmotiEffNet models for frame-level facial feature extraction and feed them into the Temporal Convolutional networks to take into account the dynamics of facial expressions. In addition, we study the possibility of combining facial processing with audio feature extraction to improve the accuracy of audio-visual expression recognition. Experimental results for two tasks from the sixth Affective Behavior analysis in-the-Wild challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our approach took third place in the action unit detection and fourth place in the expression recognition.

关键词： Measurement Gold Visualization Face recognition Conferences Transformers Feature extraction

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Automatic Privacy Detection in Scanned Document Images Based on Deep Neural networks

Automatic Privacy Detection in Scanned Document Images Based...

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2019 International Russian Automation Conference, RusAutoCon 2019

作者： Kopeykina, Lyudmila Savchenko, Andrey V. National Research University Higher School of Economics Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9781728102658

The authors consider the problem of automatic detection of private scanned documents based on text recognition with deep neural networks. The paper suggests implementing a two-phase approach with the first stage which includes efficient EAST text detection and recognition using Tesseract OCR Engine. Secondly, the authors classify the privacy of a scanned document by deep neural networks applied to the extracted text. After that, a special dataset is gathered in order to train these networks. The experiments show that using OCR Engine for both text detection and segmentation ends up with relatively poor identification of private documents when compared to preliminary text detection with EAST method. Moreover, conventional keyword spotting using the list of sensitive words is less accurate when compared to neural network-based methods. Finally, it was demonstrated that the classification of a bag of most frequent words outperforms traditional text classification techniques with LSTM and convolutional networks. © 2019 IEEE.

关键词： Deep neural networks

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Improving the Accuracy of One-Shot Detectors for Small Objects in X-ray Images

Improving the Accuracy of One-Shot Detectors for Small Objec...

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2020 International Russian Automation Conference, RusAutoCon 2020

作者： Demochkina, Polina Savchenko, Andrey V. National Research University Higher School of Economics Nizhny Novgorod Russia National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9781728161303

In this paper, we address the problem of detecting small objects on high-quality X-ray imagesusing deep neural networks. We propose to implement the two-stage approach, in which, firstly, input image issplit into partially overlapping blocks to make small objects more discriminative for detection. Secondly, the small blocks are fed into conventional single-shot detectors. These detectors are trained using the blocks of the training images extracted by the same *** datasets of X-ray images from the customs inspection complex are examined in the experimental study. It was shown thatthe proposed algorithm with data augmentationleads tomore precise results when compared to the conventional technique:ourmethod outperforms the traditional approach by 5.4 - 25.7% depending on the type of used backbone convolutional neural network. © 2020 IEEE.

关键词： Deep neural networks

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Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors

Effective face recognition based on anomaly image detection ...

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Anastasiia Sokolova Andrey V. Savchenko HSE University Nizhny Novgorod Russia Sber AI Lab Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-aged and Caucasian people, which cause most algorithms to fail on photos of older people or children, rarer ethnicities, poor-quality images, etc. Detection of such anomaly data and its subsequent rejection helps to improve the classification accuracy. We propose a novel algorithm, at the first stage of which a convolutional neural network is used to detect anomalies in input images. This network is trained on a specially created set of rare data. The second stage is the sequential analysis of neural descriptors extracted from input faces to improve the computational efficiency of classification. An experimental study on the VGGFace2 and MS-Celeb-1M datasets using neural network descriptors, including contemporary InsightFace models, demonstrated the effectiveness of the proposed algorithm.

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Sport teams logo detection based on deep local features

Sport teams logo detection based on deep local features

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2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

作者： Kuznetsov, Andrey Savchenko, Andrey Geoinformatics and Information Security Department Samara National Research University Samara Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9781728144016

Logo detection in an unconstrained environment is a challenging task. The problem lies in the small size of logos that are to be detected and different representations of logos due to the view angle, rotation angle and perspective distortions. We provide a part of a new labelled SportLogo dataset for research. In this work we also propose a solution to the task of sport logos detection for the whole range of MLB, NBA and HNL teams. Ambiguity appears when developing the detection algorithm because of a huge number of occlusions and logo view changes due to wrinkle uniform during hockey games. Another problem is logo style changes for some teams. First, we propose an approach based on local descriptors calculation (SIFT, SURF, ORB, BRISK, FREAK, AKAZE) and provide the results of conducted experiments. Second, we propose an approach based on deep local features (DELF), which are pre-Trained on Google Landmarks dataset, and compare them with classic local features. Experimental research showed that BRISK features provide the highest quality values on the labelled NHL teams dataset. © 2019 IEEE.

关键词： Sports

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An analytical expression for the distribution of the sum of random variables with a mixed uniform density and mass function

An analytical expression for the distribution of the sum of ...

引用

作者： Batsyn, Mikhail Kalyagin, Valery Laboratory of Algorithms and Technologies for Network Analysis Department of Applied Mathematics and Informatics The National Research University Higher School of Economics 136 Rodionova Street Nizhny Novgorod Russian Federation 603093 Russia

ISBN: (纸本)9781461455738

The distribution of the sum of independent random variables plays an important role in many problems of applied mathematics. In this chapter we concentrate on the case when random variables have a continuous distribution with a discontinuity (or a probability mass) at a certain point r. Such a distribution arises naturally in actuarial mathematics when a responsibility or a retention limit is applied to every claim payment. An analytical expression for the distribution of the sum of i.i.d. random variables, which have a uniform distribution with a discontinuity, is reported. © Springer Science+Business Media New York 2013.

关键词： Random variables

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Marine mammal calls detection in acoustic signals via gradient boosting model 6

Marine mammal calls detection in acoustic signals via gradie...

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6th Underwater Acoustics Conference and Exhibition, UACE 2021

作者： Salin, Mikhail Ponomarenko, Alexander Center of Hydroacoustics Institute of Applied Physics RAS Nizhny Novgorod603950 Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Niznhy Novgorod Russia

This paper is devoted to methods of processing hydrophone records in order to identify specific signals, produced by marine mammals. The aim of processing is to detect a signal according to a certain pattern against the background of non-stationary noise. Development of a robust detection algorithm is of great interest because it could help to assess marine mammal occurrence and distribution at greater temporal and spatial scales by excluding laborious task of manually analyzing acoustic data. In addition, such algorithm may be implemented in automatic systems designed to warn vessel crew to avoid potential collision with whales. The data, studied in this paper, contained the calls of North Atlantic right whales, that were collected in the Gulf of St. Lawrence. Machine learning is exploited to solve a signal detection problem. The model was trained by a set of manually labelled signal fragments. Each fragment is labelled whether a whale call is present or not in the fragment. The proposed detection method is based on the two-level stacked gradient boosting models (XGBoost algorithm). The first-level model deals with spectral features, extracted from the short signal frames. The second model accumulates estimated output for a group of subsequent frames. © 2021 Acoustical Society of America.

关键词： Signal detection

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Pose networks Unveiled: Bridging the Gap for Monocular Depth Perception

Pose Networks Unveiled: Bridging the Gap for Monocular Depth...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Yazan Dayoub Andrey V. Savchenko Ilya Makarov HSE University Laboratory of Algorithms and Technologies for Network Analysis Sber AI Lab HSE University ISP RAS Research Center for Trusted Artificial Intelligence Artificial Intelligence Research Institute (AIRI) HSE University

ISBN: (数字)9798331506919

ISBN: (纸本)9798331506926

Depth estimation is essential in Augmented Reality applications, enabling realistic object placement, scene understanding, spatial mapping, interaction, and environment awareness. This paper proposes a method to enhance depth model performance without increasing inference costs by improving the pose network in a self-supervised learning setup. In particular, we enrich spatial information in the pose network by incorporating features from different scales and normalized coordinates. It is experimentally shown on the KITTI dataset that our approach achieves a 2-7% improvement in the abs_rel metric when compared to baseline techniques.

关键词： Accuracy Costs Computational modeling Pose estimation Self-supervised learning Cameras Encoding Augmented reality Load modeling

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