Problem Of Person Identification Research Articles

Identifying persons from iris images using neural networks for image segmentation and feature extraction

The problem of personal identification plays an important role in information security. In recent years, biometric methods of personal identification have become most relevant and promising. The article presents a study of a method for identifying a person from iris images using a neural network approach at the stages of segmentation and a feature representation from the data. A description of a dataset used to implement the segmentation stage using convolutional neural networks is presented and access to the segmentation masks of the entire dataset is provided. A method is proposed for extracting a feature representation of the data using pretrained convolutional neural networks to solve a problem of iris classification. A comparative analysis of methods for extracting iris features, including classical approaches and a neural network approach, has been carried out. A comparative analysis of classification methods is carried out, including classical machine learning algorithms, namely, support vector machines, random forest, and a k-nearest neighbors method. The results of experimental studies have shown the high quality of the classification based on the proposed approach.

Algorithm for extraction of identification features in ear recognition

The algorithm for feature extraction with the help of cosine transformation in ear recognition is presented. The software was developed on the base of proposed algorithm. The proposed algorithm was tested on solving the problem of person identification by ear images. Collecti on does not have an associated hygiene issue, as may be the case with direct contact fingerprint scanning, and is not likely to cause anxiety, as may happen with iris and retina measurements.

Heart signatures: Open-set person identification based on cardiac radar signals

ObjectiveNon-contact continuous biometric identification system based on the heartbeat signals has attracted more attention due to its privacy-friendly properties. Current methods, however, mostly focused on the traditional machine learning methods under the close-set condition. This paper aims to investigate the feasibility of using the cardiac radar heartbeat signals and deep learning techniques to identify person in the open-set environment. MethodsA novel dipole deep learning model (DDLM) was proposed for the open-set person identification problem without heartbeat segmentation and feature engineering. The normalized heartbeat samples with time duration of 5 s were used as input and encoded into the feature space, where the encoded features of the same person cluster closely around the corresponding negative pole and repel far from positive pole, and those of different persons separate loosely from each other. Finally, threshold on the distance from the features to the dipoles in the feature space was set for each known identity. ResultsExtensive experiments conducted on a public dataset of clinically recorded vital signs indicate that:(1) The proposed model shows high stability under close-set condition with an accuracy higher than 99 % with 30 subjects.(2) The accuracy and the F1-score attain 93.42 % and 93.57 % under the open-set condition with the maximum openness of 29.3 %, respectively. ConclusionThe proposed model shows high effectiveness in person identification using heartbeat signals. The DDLM outperforms most of the existing methods under the close-set condition. And the DDLM shows a promising future for person identification in open-set environment.

Об отношении личности к самой себе

The article is devoted to the problem of personal identification. The interaction of people in modern society is based on the dependence of one person on another and the need of one for another. A person forms his identity under the influence of both norms and values that are common to the culture as a whole and special for the subculture. It is shown that a person can be a subject of social relations only to the extent that his activity merges with the activities of other people who are part of a historical community or a stable association of people. In other words, an individual is a true subject of social relations, the creator of reality and an active principle only because such a subject is the community into which he enters and whose interests are objectively determined by his activity. Thus, identification is not a free, but a socially conditioned choice of belonging to a certain group.

Classification Algorithm for Person Identification and Gesture Recognition Based on Hand Gestures with Small Training Sets.

Classification algorithms require training data initially labelled by classes to build a model and then to be able to classify the new data. The amount and diversity of training data affect the classification quality and usually the larger the training set, the better the accuracy of classification. In many applications only small amounts of training data are available. This article presents a new time series classification algorithm for problems with small training sets. The algorithm was tested on hand gesture recordings in tasks of person identification and gesture recognition. The algorithm provides significantly better classification accuracy than other machine learning algorithms. For 22 different hand gestures performed by 10 people and the training set size equal to 5 gesture execution records per class, the error rate for the newly proposed algorithm is from 37% to 75% lower than for the other compared algorithms. When the training set consists of only one sample per class the new algorithm reaches from 45% to 95% lower error rate. Conducted experiments indicate that the algorithm outperforms state-of-the-art methods in terms of classification accuracy in the problem of person identification and gesture recognition.

Социальная нормативность и персональная идентификация: соотношение понятий и практическая реализация

The research objective includes identifying correlation of social normativeness and personal identification. The article examines correlation of the basic notions: personality, identity, identification, norm, social norm. Scientific originality of the study lies in the fact that the problem of personal identification is considered through the lenses of normativeness as a social phenomenon. It is stated that a person acquires features in the process of social norms assimilation thus establishing social ties and relations. Just social normativeness largely determines personal identity which includes social and personal components. The findings indicate that an individual’s personal identification occurs when they identify themselves with the adopted standards of behaviour. During this process, a collision of social expectations and personal aspirations occurs. This contradiction can be removed by different methods, one of which is art.

Person Recognition Method in Cross-Country Environment Based on Improved Euclidean Clustering

Automated unmanned vehicles can carry equipment for teams, greatly reducing the load of person. Most of the working environment of unmanned vehicles is in the field. The identification of persons in a cross-country environment is the basic requirement for automated driving vehicles, and the problem of identification has received much attention. Aiming at the problem of person identification from lidar point cloud data, particularly the special problem of identification in a cross-country environment, this paper designs an improved identification algorithm based on Euclidean clustering, theoretical analysis, and the geometric and physical characteristics of people. Experiments are carried out on tracked vehicle platforms in a cross-country environment to verify the performance of the algorithm. The experimental results show that the designed lidar personnel identification algorithm can accurately identify personnel using lidar point cloud data, and the recognition rate is about 5% higher than that before the improvement.

КОНСТРУИРОВАНИЕ ПРОЦЕССОВ ИДЕНТИФИКАЦИИ И САМОИДЕНТИФИКАЦИИ ЛИЧНОСТИ В НАТУРАЛИСТИЧЕСКОМ ПОДХОДЕ

Issues of identity formation affect the essential foundations of human being in the world. The identification processes carried out by a person allow him/her to self-determine in society, to understand his/her own place in the structure of social relations and the purpose of his/her life. To date, science has no unambiguous understanding of the essence of identification. In this regard, this article is aimed at identifying the bases and factors of the processes of identification. In this study, the author mainly used a naturalistic approach to the understanding of the processes of identification, since it offers the basis for identification to consider the individual need of a person for self-determination. Based on the methods of comparative analysis, synthesis, generalization and abstraction, the basic concepts on the problem of identification were compared, the general and the specific in the points of view of various authors were identified, which contributed to a more thorough and detailed study of the phenomenon of identification. Identification is a process of interaction with the external, mainly social environment, during which a person identifies with objects and phenomena of the external world. The basis of identification, as shown by the study of most of the concepts available in science, is the human need for self-recognition. It is this need that initiates the process itself. However, in the course of identification, the sociocultural context plays a crucial role, in which a person recognizes oneself in others by searching for common foundations of being. In turn, selfidentification allows comparing the components of oneself with each other via thought communication. In unity, the processes of identification and self-identification form the identity of a person, both to oneself and to society. Identity initiates behavioral manifestations. In itself, it is unstable, since it is influenced by the external social environment. Identification can occur both for several reasons, and for many. Moreover, the sense of identity can be both actual, based on the analysis of the qualities of objects of the external world and their comparison with their own characteristics, and fictitious, formed as a result of inspiring the individual to be convinced of similarity. In the latter case, we are talking about manipulation. Identity is a prerequisite for the successful socialization of an individual, as it allows not only identifying identity with society, but also realizing its integrity and isolation from it. All this allows a person to function and develop harmoniously. The materials of the article are of practical value, since they can be used as a theoretical and methodological basis for the further development of the problem of personal identification in the framework of sociophilosophical or wider humanitarian research.

Video analytics-based intelligent surveillance system for smart buildings

The goal of the work is to automate video surveillance. The work holds its importance since the camera surveillance under manual supervision fails occasionally. Face images of authorized users in the building are trained, and for each face image, weight is calculated. When the test face comes into the building, the weight for the test face is calculated and compared with the existing weights. Based on the similarity between them, the person’s face is identified. For successful recognition of face across the frames, first face image has to be detected. To handle this task, we propose a hybrid algorithm using Haar cascade classifier and skin detector. The stand-alone performance of Haar cascade classifier and skin detector is analyzed, and the work discusses the need for hybridization. The proposed approach addresses the challenges in detecting faces such as orientation changes, varying illumination and partial occlusion. The performance of the system is comparatively analyzed with the videos of frontal and pose-varying faces, and the detection of face across frames is measured. From the experimental analysis, we infer that the detection rate of the proposed hybrid algorithm is 100% for frontal face video and 99.895% for pose-varying face video. The proposed hybrid algorithm is tested with VISOR dataset, and the proposed algorithm achieves precision of 95.20%. We also propose a deep learning framework based on the hybrid algorithm to detect face across the frames. The proposed framework generates more images by affine wrapping strategy and thus handling face orientation changes. The test data are labeled based on counting the prediction results of all the affine transformed images. To evaluate the performance of the system, the framework is tested with frames from VISOR dataset. From the experimental analysis, we infer that the precision of the proposed deep learning framework is 99.10%. Since the person’s face has been detected across the maximum number of frames, the next focus of the work is to identify the person whose face has been detected. For addressing the person identification problem, we use principal component analysis to reduce the dimension of the face vectors. The weight matrix or confidence for every face vector is calculated and checked for similarity with the weight matrix of test face vector. Based on the similarity between the two weight matrixes, a person’s face is identified. The built automated surveillance system is tested with NRC-IIT facial video database, from the experimental analysis, we infer that the system detects face across all the frames and out of ten videos, the system correctly identifies the face of the person in nine videos so that we calculate the recognition rate as 90%.

Dynamical system classification with diffusion embedding for ECG-based person identification

The problem of system classification consists of identifying the source system corresponding to a certain output signal. In the context of dynamical systems, the outputs are usually given in the form of time series, and this identification process includes determining the underlying states of the system or their intrinsic set of parameters. In this work we propose a general framework for classification and identification based on a manifold learning algorithm. This data-driven approach provides a low-dimensional representation of the system's intrinsic variables, which enables the natural organization of points in time as a function of their dynamics. By leveraging the diffusion maps algorithm, a particular manifold learning method, we are not only able to distinguish between different states of the same system but also to discriminate different systems altogether. We construct a classification scheme based on a notion of distance between the distributions of embedded samples for different classes, and propose three ways of measuring such separation. The proposed method is demonstrated on a synthetic example and later applied to the problem of person identification from ECG recordings. Our approach obtains a 97.25% recognition accuracy over a database of 90 subjects, the highest accuracy reported for this database.

LYCEUM EDUCATION IN CONTEMPORARY RUSSIA

The concept-theoretical problems of lyceum education are investigated. Lyceum as a type of educational institution is observed as a cultural phenomenon. Educational activities are examined from the cultural point of view that creates conditions for spiritual and moral upbringing, patriotic education, based on the true feelings and attitudes. The aim of the investigation is to identify the essential characteristics of lyceum education, its values, ideals and goals of development, its synergy potential in the innovative development of the whole education sector. Methods . Historical, cultural and pedagogical analysis of modern lyceum education from the point of the development of democratic goals and global challenges was submitted. The problem of personal identification in the system of social relations and cultural processes is analyzed. The problems of scientific knowledge about the lyceum education and their bases are studied. Traditions of lyceum education are analyzed. It is indicated that they give the coordinates of scientific thinking that require a philosophical hermeneutic approach to the understanding of modern life and the analysis of contradictory processes and phenomena of reality that provide the nurturing and development of the individual. The results of research have heuristic value. The creation of new schools is based not only on the search for a universal, repetitive, but on the study of special, exceptional, which is not only now seize the minds of mankind. It’s required to anticipate the expectations of the future, based on the spiritual experience of humanity in order to grasp the unknown, which can be revealed only in the most general outlines. Scientific novelty. The definition of lyceum education is given and modern features are highlighted. The technology of training professional and social leaders is identified. Practical significance. Historical, cultural and pedagogical analysis of realworld experience creating high schools and prospects of development of this type of education in modern society is given. The practical significance of the study lies in the fact that lyceum is considered broader than the school with in-depth study of specific subjects, but as a center of reflection and formation of innovative experience.

Enhancing gait based person identification using joint sparsity model and [formula omitted]-norm minimization

We consider the problem of person identification using gait sequences under normal, carrying bag and different clothing conditions as the main concern. It has been demonstrated that Gait Energy Image (GEI) can attain a better gait recognition rate under normal conditions. However, it has been shown that GEI is not robust enough to handle the carrying bags and different clothing conditions. Instead of GEI, there are several appearance based gait features in the available literature to reduce the effect of covariate factors by keeping dynamic parts and removing the static parts of the gait features under the assumption that the carrying bags and different clothing conditions affect mostly the static parts. It is however shown in the literature that the static parts also contain valuable information and removal of certain static parts such as head by mistake thigh typed certainly decreases the recognition rate.Our main objective has been to increase the gait recognition rate on different clothing and carrying bag covariate gait sequences. Therefore instead of removing static parts, the Joint Sparsity Model (JSM) is applied to identify the carrying bags and different clothings conditions from GEI features. If a set of GEI feature vectors is submitted to JSM model then a common component and an innovations component for each GEI feature are obtained. The innovations component that has unique characteristic to each of features is considered to identify the covariate conditions. The identified covariate conditions are removed from GEI features and a novel gait feature called GEIJSM is generated. The dimension of GEIJSM is reduced using Random Projection (RP) approach and ℓ1-norm minimization technique based sparse representation is used for classification. It is demonstrated that the RP and ℓ1-norm minimization based sparse representation approach provides statistically significant better results than that of the existing individual identification approaches.

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During the information exchange from one department to another the problem of personal identification arises. This problem concerns people who have partially or completely not coinciding personal details. In represented work the new method and algorithm for such people identification are elaborated. This method is based on fuzzy comparison and on the metrics of Levenshtein. The algorithm, developed in the form of Data Mining process, allows defining people quickly according to earlier carried out search. The built-in system of personal details priority gives the opportunity to identify person in such cases as changing of surname, name, moving, mistakes from manual data input and if personal details are partially absent also. The algorithm was realized by PL-SQL in Oracle 11g database management system.

Context-based person identification framework for smart video surveillance

Smart video surveillance (SVS) applications enhance situational awareness by allowing domain analysts to focus on the events of higher priority. SVS approaches operate by trying to extract and interpret higher "semantic" level events that occur in video. One of the key challenges of SVS is that of person identification where the task is for each subject that occurs in a video shot to identify the person it corresponds to. The problem of person identification is especially challenging in resource-constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high-quality data. Conventional person identification approaches which primarily are based on analyzing facial features are often not sufficient to deal with poor-quality data. To address this challenge, we propose a framework that leverages heterogeneous contextual information together with facial features to handle the problem of person identification for low-quality data. We first investigate the appropriate methods to utilize heterogeneous context features including clothing, activity, human attributes, gait, people co-occurrence, and so on. We then propose a unified approach for person identification that builds on top of our generic entity resolution framework called RelDC, which can integrate all these context features to improve the quality of person identification. This work thus links one well-known problem of person identification from the computer vision research area (that deals with video/images) with another well-recognized challenge known as entity resolution from the database and AI/ML areas (that deals with textual data). We apply the proposed solution to a real-world dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even on low-quality video data.

Comparison of face profiles based on homeomorphism

A solution to the problem of person identification by facial profile is proposed. The approach includes the extraction of the profile from an image, building a profile vector representation, and comparing the profiles on the basis of homeomorphism. Results of the experiments estimating the recognition rate, optimizing the parameter values of the method, and testing its sensitivity to input data variation are reported.

Comparative analysis of the computational geometry and neural network classification methods for person identification purposes via the EEG : Part 1

In this paper we address the problem of person identification via features extracted from the electroencephalogram (EEG). For this problem, our work investigates appropriate feature extraction and classification methods. Our previous research into feature extraction produced a high number variety of specific EEG features, of varying dimensionalities. However, it is known that traditional classification methods such as neural networks are inefficient at classifying this type of feature. For this reason, in the present paper, we develop a heuristic classifier based on Computational Geometry algorithms. In order to exhibit the potential of the proposed method we compare it with a conventional Radial Basis Function neural classifier using the same (pre-) processing feature extraction methods in both cases.

Person Identification from the EEG using Nonlinear Signal Classification

This paper focusses on the person identification problem based on features extracted from the ElectroEncephaloGram (EEG). A bilinear rather than a purely linear model is fitted on the EEG signal, prompted by the existence of non-linear components in the EEG signal--a conjecture already investigated in previous research works. The novelty of the present work lies in the comparison between the linear and the bilinear results, obtained from real field EEG data, aiming towards identification of healthy subjects rather than classification of pathological cases for diagnosis. The EEG signal of a, in principle, healthy individual is processed via (non)linear (AR, bilinear) methods and classified by an artificial neural network classifier. Experiments performed on real field data show that utilization of the bilinear model parameters as features improves correct classification scores at the cost of increased complexity and computations. Results are seen to be statistically significant at the 99.5% level of significance, via the chi 2 test for contingency. The results obtained in the present study further corroborate existing research, which shows evidence that the EEG carries individual-specific information, and that it can be successfully exploited for purposes of person identification and authentication.