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Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors
P. 1–5.
Sokolova A., Savchenko A.
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.
Publication based on the results of:
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Ivanov P., Shtark M., Kozhevnikov A. et al., IEEE Access 2025 Vol. 13 P. 25186–25197
Fault detection and diagnosis (FDD) is a critical challenge in industrial processes aimed at minimizing risks such as safety hazards, costly downtime, and suboptimal production. Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance ...
Added: April 29, 2025
Sergeev A., Minchenkov V., Soldatov A. et al., ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING 2025 No. 1 P. 3344–3355
The automatic monitoring of manual assembly processes in production settings increasingly relies on advanced technologies, including computer vision models. These models are designed to detect and classify events such as the presence of components in an assembly area and the connection of these components. However, a significant challenge for detection and classification algorithms is their vulnerability ...
Added: April 2, 2025
V.P. Stepashkina, M.I. Hushchyn, Doklady Mathematics 2024 Vol. 110 No. 1 P. S95–S102
This paper presents the development and evaluation of methods for detecting cyberattacks on industrial systems using neural network approaches. The focus is on the task of detecting anomalies in multivariate time series, where the diversity and complexity of potential attack scenarios require the use of advanced models. To address these challenges, a transformer-based autoencoder architecture ...
Added: March 25, 2025
Savchenko A., Maslov D., Makarov I., , in: ECAI 2024. 27th European Conference on Artificial Intelligence, October 19 – 24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024).: IOS Press, 2024. P. 4439–4442.
Added: February 15, 2025
Savchenko A., Savchenko L., Makarov I., IEEE Access 2023 Vol. 11 P. 65977–65990
This paper addresses the face recognition task for offline mobile applications. Using AutoML techniques, a novel technological framework is proposed to develop a fast neural network-based facial feature extractor for a concrete device. First, the Once-for-All SuperNet is trained on a large facial dataset. Each device is characterized by its lookup table, which contains the ...
Added: August 28, 2023
A. V. Savchenko, L. V. Savchenko, Information Sciences 2023 Vol. 648 Article 119540
This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame ...
Added: August 27, 2023
Sokolova A., Savchenko A., , in: 2022 International Joint Conference on Neural Networks (IJCNN).: Institute of Electrical and Electronics Engineers Inc., 2022.
One of the main issues in face identification is to create a real-time application with high accuracy. Images are presented by high-dimensional feature vectors that are produced by convolutional neural networks. In order to effectively process such vectors, the hierarchical algorithm was proposed in this paper that applies sequential analysis to search the nearest neighbors ...
Added: May 29, 2023
Churaev E., Savchenko A., Software Impacts 2023 Vol. 16 Article 100507
Nowadays, many meetings, lessons, conferences, and presentations are organized online, where it is complicated to communicate with an audience and control their engagement and emotions. In this article, we present a novel C++ application that is led to help estimate facial identities and expressions. It captures a screen with a window of an arbitrary online ...
Added: May 18, 2023
Alexandrov D., Программная инженерия 2022 Vol. 13 No. 7 P. 331–343
Trends in computer vision and pattern recognition and capabilities of modern computers contributed to a consid- erable amount of research of these areas application in facial recognition systems. The purpose of this paper is to investigate the most significant methods of face recognition. In the first two sections of current paper, the methods of face ...
Added: October 31, 2022
Manna S., Ghildiyal S., Bhimani K. R., IEEE Access 2020 Article 1
Face recognition (FR) and verification is the immeasurable technology to encounter any criminal activities nowadays. With the remarkable applications extending from criminal ID, security, and observation to amusement sites. This system (recognition of faces) is exceptionally helpful in banks, air terminals, and different associations for screening customers. In deep learning, convolutional neural networks (CNN) have ...
Added: October 14, 2022
Savchenko A., Belova N. S., Expert Systems with Applications 2022 Vol. 207 Article 117885
In this paper, the computational complexity of the probabilistic neural network for the classification of high-dimensional data is improved. At first, the class probability densities are estimated by using only a few principal components of an observed point. The Gaussian–Parzen kernel is replaced by the orthogonal series estimates of class-conditional densities for each principal component using the Fourier series to speed ...
Added: June 29, 2022
Demochkina P., Savchenko A., , in: 2021 International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2021. P. 1–5.
In this paper, we propose to solve the problem of facial expression recognition in videos by implementing a two-stage procedure, in which, firstly, facial features are extracted from all frames using an EfficientNet-based model. The latter is pre-trained to identify facial attributes and further fine-tuned on an external dataset for the emotion classification task. Secondly, ...
Added: April 10, 2022
Springer, 2021.
This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover ...
Added: April 10, 2022
Салин М. Б., Ponomarenko A., , in: Proceedings of Meetings on AcousticsVol. 44. Issue 1: 6th Underwater Acoustics Conference and Exhibition.: [б.и.], 2021.
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 ...
Added: December 10, 2021
Ryzhikov A., Borisyak M., Ustyuzhanin A. et al., PeerJ Computer Science 2021 Vol. 7 Article e757
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus ...
Added: November 30, 2021
Markvirer V., Ulitina S., , in: Development of Science = Развитие науки : материалы конкурса исследовательских работ на английском языке (2020–2021 г.).: ПГКУБ им. А. М. Горького, 2021. P. 66–72.
The article presents analytical review of existed solutions and technologies applied in computer vision control access systems, video monitoring and analysis areas. Such technologies are parts of the smart city concept and commonly used for recognition of faces in modern office buildings and business centers. Face recognition is used to distinct employees and guests, separated ...
Added: September 20, 2021
Pustokhina, I.V., Pustokhin D. A., Vaiyapuri T. et al., Safety Science 2021 Vol. 142 Article 105356
Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes ...
Added: September 20, 2021
Kharchevnikova A., Savchenko A., PeerJ Computer Science 2021 Vol. 7:e391 P. 1–18
The article is considering the problem of increasing the performance and accuracy of video face identification. We examine the selection of the several best video frames using various techniques for assessing the quality of images. In contrast to traditional methods with estimation of image brightness/contrast, we propose to utilize the deep learning techniques that estimate ...
Added: February 25, 2021
Savchenko A., Записки научных семинаров ПОМИ РАН 2021 Т. 499 С. 267–283
In this paper fast image recognition techniques based on statistical sequential analysis are discussed. We examine the possibility to sequentially process the principal components and organize a convolutional neural net- work with early exits. Particular attention is paid to sequentially learn multi-task lightweight neural network model to predict several facial at- tributes (age, gender and ...
Added: January 27, 2021
Shalileh S., Shahdi S. O., , in: 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP).: IEEE, 2017. P. 31–38.
In recent years many research works have been devoted either to anomaly detection or anomaly classification. However, very few of them address both of them simultaneously. In this paper, we introduced a new method not only to detect and localize the abnormalities in crowded scenes but also to determine the class of abnormality. In This ...
Added: January 13, 2021
Bryksin T., Petukhov V., Alexin I. et al., , in: MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories.: ACM Press, 2020. P. 455–465.
In this work, we apply anomaly detection to source code and byte-code to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, ...
Added: November 17, 2020