X-MyoNET: Biometric Identification Using Deep Processing of Dynamic Surface Electromyography

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.

This paper considers various personal identification systems based on questions and answers concerning knowledge inherent to the individual, and discusses the personal identification probability. Assume a personal identification system which contains N knowledge items and assume that a solver has forgotten the correct answers for M items after the registration. The behavior of the system is as follows: Q knowledge items for questions are selected randomly from N items. For each of the selected items, R selection keys are presented which include a correct answer and the solver is requested to select one. When the solver forgets the correct answer, the probability of correct answer is 1/R; otherwise it is 1. The following three systems are considered under such conditions. System I decides the solver as correct if all answers to the extracted Q items are correct. System II decides the solver as correct if there are fewer than K incorrect answers. System III decides the solver as correct unless k or more consecutive answers are incorrect. It is seen that the probability of personal identification increases in the order of I, II and III. It is not always true that the probability of excluding an incorrect solver is low if the probability of personal identification is high. Each of the considered systems is suited to particular applications, depending on the environment.

Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.Results. The results can be used for further development of automated access control and management systems using biometric identification.Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

The importance of automatic person identification using non-intrusive biometric modality has created enormous interest in computer vision society over the last few years. For this, gait based person recognition is receiving much more attention in different applications like visual surveillance, security control, people counting. In this paper, we have presented a gait based person identification system using 3D human pose modeling for any arbitrary walking pattern in any unrestricted indoor environment, using Microsoft Kinect sensor. Instead of estimating gait cycle, we have modeled the gait pattern with a spatiotemporal set of key poses and sub-poses which occur periodically in different gait cycles. The robustness of the solution is increased by outlier detection to handle noisy skeleton data obtained from Kinect. The performance of the proposed system is also assessed with rotating Kinect setup to increase the field of view of single Kinect. We have done the average and worst case performance evaluation of the system with respect to the existing Kinect based approaches. It needs to be mentioned that our proposed person identification system is able to achieve a frame level F-score of more than 90% for 20 subjects with fixed Kinect setup.

Now a day’s security and identification of a person are crucial in real time applications and solving the problems in biometric identification. Objectives: To address the issues in real time, the face and its organ’s identification are the main parts of the human body. The proposed work involves extraction of the face of the real time captured image, after extraction of the face, the organs like eyes, mouth and nose is extracted for identification of exact person. Methods/ Analysis: The present work captures the real time image from cameras or moving video devices for facial recognition using Principal Component Analysis (PCA), the facial features are extracted from both Hidden Markov Model (HMM), Gaussian mixture model (GMM) methods and classified into different organs using Artificial Neural Network (ANN). The features of organs are extracted in different stages, in first stage Eigen values with the help of PCA, second and third stage feature extraction with help GMM and HMM since these three techniques are the most powerful tools for statistical natural image processing. After extraction of organs of the face, the ANN is applied for classifications of eyes, mouth and nose separately. Findings: In the proposed work the facialorgans are separated into threeslight scale images and these are recombined to acquire the appreciationfacial image results like mouth, eyes and nose. The final obtained results shows that the proposed method has been achieved 95.8% recognition accuracy, Fault Rejection Ratio (FRR) is about 93.1% and Fault Acceptance Ratio (FAR) is 1.7 % which are implemented in Matlab2013A.

This paper presents an intelligent system for person identification with biometric measures such as signature, fingerprint and face. We describe the neural network architectures used to achieve person identification based on the biometrics measures. Simulation results show that the proposed method provides good recognition.

In this paper an improved face detection method based on our previous Information-Based Maximum Discrimination approach is presented that maximizes the discrimination between face and non-face examples in a training set without even using color or motion information. A short review of our previous method is given together with the description of the intuition behind and our recent improvement on its detection speed. A person identification system has been developed that performs multi-modal person identification in real-time video based on this newly improved face detection method together with speaker identification.

The heart electrical activity has highly distinctive characteristics for each individual, which motivated us to use the electrocardiogram signal (ECG) as a biometric characteristic. This paper presents a new approach for human recognition from their Electrocardiograms (ECG). The proposed approach is based on an extraction feature with fiducial detection in time domain. It consists of three steps. After the preprocessing step, we extracted ten morphological descriptors and sixty hermite Polynomials Expansion coefficients (HPEc) from each heartbeat which represented the second step. The classification step is done by using a binary SVM and the hidden Markov model (HMM). In this step, a particular strategy of persons' identification was adopted: Firstly, we used four groups of morphological descriptors separately. Secondly, we combined them in one input vector of our classification system. Moreover, we employed the hermite Polynomials Expansion coefficients apart. In order to improve performance, we associated the HPEc with all groups of morphological descriptors as input parameters of our classification system. We tested our algorithm on 18 healthy signals of the MIT_BIH database. The results of experiments showed that the proposed hybrid approach has achieved an identification rate equal to 98.97% using SVM and 99.02% using HMM.

Social games offer a rich and challenging environment to develop and evaluate complex robot behaviors. Social interactions with robots benefit from personalization, which requires person identification—a process that raises privacy concerns. Soft biometrics, however, offer a privacy-preserving alternative by enabling short-term identification without relying on private or sensitive features. In this work, we leverage a zero-shot Visual Question Answering (VQA) person identification system and adapt it to make Pepper play Guess Who? The identification module performance is firstly studied offline in the AveRobot dataset. Afterwards, it is integrated in a ROS based architecture that manages the game flow and enables natural verbal interaction. Experiments conducted with 29 subjects demonstrate that the system achieves identification performance nearly equivalent to that of hard biometric-based systems while also offering engaging and entertaining gameplay. Notably, user feedback reveals a high level of acceptance, highlighting that social gaming with robots is a promising avenue for developing and testing complex behaviors.

A real-time automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. We introduce a medium-size realtime automatic personal identification system, F2ID, which integrates faces and fingerprints to make a personal identification. F2ID overcomes some of the limitations of face recognition systems and fingerprint verification systems and can achieve a desirable identification accuracy with a tolerable response time. We have tested our system on a limited set of face and fingerprint images collected in a laboratory environment. Experimental results show that that our system meets both the identification accuracy as well as the speed requirements.

Monitoring and evaluation algorithm of GNSS signal in space availability

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Biometrics focuses on simulate the human ability to associate one or a set of corporal features of a person in a unique way by uses a specific representation, this representation is knows as identity. Visible spectrum face recognition is the identification way more natural which has a higher universality, collectability and acceptability front to the other biometrics modalities, but is weak in the invariance and distinctiveness criterion to be a good biometric. In order to improve the face recognition, the infrared spectrum arises as a good representation to solve these drawbacks in biometric identification. Buddharaju et al., proposed a process by which the vascular net is detected [4] . However, Wu et al., criticized this approach by not take into account the heat transfer between the environment and the person in the time to take the image and proposed a modification of image thermogram and show that it is a better solution to make up for the heat change [19, 21]. This paper is written intending to know if there is a significant difference between both approach to be used as biometric representation. We found that the normalization of the thermograms, proposed by Wu, do not affect the distinctive zones of high blood perfusion to be used as biometric representation.