EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.

Study of Ear Biometrics Based Identification System Using Machine Learning

Within the biometric industry, computerized person identification using ear pictures is a hot topic. The ear, like other biometrics like the face, iris, and fingerprints, contains a huge number of particular and unique traits that may be used to identify a person. Due to the mask-wearing scenario, most face detection methods fail in this present international COVID-19 pandemic. The eardrum is a great data source for inactive person authentication since it doesn't necessitate the person we're attempting to pinpoint to cooperate, and the structure of the ear doesn't change significantly over time.. The acquisition of a human ear is also simple because the ear is apparent even while wearing a mask. An ear biometric system can enhance other biometric technology in an automated person identification system by giving authentication cues when other information is unreliable or even missing. We provide a six-layer deep convolutional architecture for ear identification in this paper. On the IITD ear dataset, the deep network's potential efficiency is assessed. The IITD has a detection performance of 97.36 percent for the deep network model and 96.99 percent for the IITD. When paired with a competent surveillance system, this approach can be beneficial in identifying people in a large crowd. Keywords: Biometrics, Person identification, IIT-D, Deep learning, Ear dataset

FEATURES OF USING A TWO-DIMENSIONAL BARCODE FOR CLASSIFYING THE PERSONALITY OF A PRISONER: A COMPARATIVE ANALYSIS OF THE APPLICATION IN THE RUSSIAN FEDERATION AND FOREIGN COUNTRIES

В настоящее время набирает популярность использование на территории организаций, аэропортов и в других сферах, в том числе в исправительных учреждениях Российской Федерации (далее - ИУ РФ) и зарубежных стран двухмерного штрих-кода для передачи информации. Безусловно, применение данного штрих-кода в биометрической идентификации личности имеет огромное преимущество перед осуществлением аналогичной деятельности непосредственно сотрудниками уголовно-исполнительной системы Российской Федерации (далее - УИС), которое выражается в усиленном контроле пропуска и безопасности сотрудников и осужденных от несанкционированного прохода посторонних лиц. Биометрическая идентификация личности производится путем сканирования сетчатки глаза, отпечатков пальцев, сканирования биометрии лица, измерения температуры тела и распознавания голоса. Однако даже такая современная система имеет определенные недостатки, выявленные специалистами в сфере инженерно-технического обеспечения, которым посвящен ряд научных работ, рассмотренных в данной статье. Также авторами проанализированы основные разновидности 2D-кодов, такие как Stackedlinear и Matrixcode. Отмечается, что 2D-кодировка применяется во многих отраслях: при производстве, транспортировке грузов, идентификации личности, шифровки данных документов и отчетов, проведении инвентаризации. Nowadays using a two-dimensional barcode for transmitting information becomes more popular in the territory of organizations, airports, and in other spheres such as correctional facilities of the Russian Federation (hereinafter - CF RF) and in abroad counties. Undoubtedly, the application of this barcode in biomedical identification of personality has a huge advantage over similar activities, which has been realized by penal officers (hereinafter - FPS ). This dignity includes enhanced control and safety of employees and convicts from an unauthorized passage of unauthorized persons. Biometric identification of personality conducted by retinal scan, fingerprint scan, facial biometrics scan, body temperature measurement, and voice recognition. However, even such a system has several disadvantages, which were identified by engendering specialists. Lots of scientific works are dedicated to these flaws, which we are going to consider in the article. Also the main varieties of 2D codes were analysed in this article, such as Stackedlinear and Matrixcode. It was found out that 2D coding is used in many different industries: in the process of production, transportation of goods, person identification, encryption of these documents and reports, inventory.

Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network

In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection network (CADCNN net) approach. Channel attention mechanism (CA) is used to handle the channel information from the EEG, while convolutional neural dense connection network (DCNN net) extracts the unique biological characteristics information for PI. The proposed method is evaluated on the state-of-the-art affective data set HEADIT. The results indicate that CADCNN net can perform PI from different affective states and reach up to 95%-96% mean correct recognition rate. This significantly outperformed a random forest (RF) and multilayer perceptron (MLP). We compared our method with the state-of-the-art EEG classifiers and models of EEG biometrics. The results show that the further extraction of the feature matrix is more robust than the direct use of the feature matrix. Moreover, the CADCNN net can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

In Your Face: Person Identification Through Ratios and Distances Between Facial Features

These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.

Person Identification Using Face and Speech Recognition for Visually Challenged with Mask Detection

A system and method for facilitating a visually impaired person for identifying a person. The method includes the step of storing a plurality of instructions for facilitating the visually impaired person identify the person in front of them by their face and/or voice characteristics by updating our project with Mask Detection using OpenCv and keras. It includes the step of receiving voice signals from the person present in surrounding of the visually impaired person and includes the step of capturing the pictures of a particular person and their surroundings of the visually impaired person and storing the processed data into the database or any storage devices. The data will be processed to AWS server or any local storage for processing and determining the person with the help of the database we already have. After processing and identifying the person with the help of face and voice recognition modules the name is sent to the visually impaired user’s phone in the form of a text message which will be read aloud by his phone’s virtual assistant.

Object Detection in Deep Surveillance

Object detection is a key ability required by most computer visions and surveillance applications. Pedestrian detection is a key problem in surveillance, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art deep neural network-based framework for object detection models. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it but will also return their relative positions. The size of these bounding boxes can vary depending on the object and it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been performed in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). The experimental analysis has been performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all the other models with 61% precision and 44% of F measure value.