Human Recognition System Research Articles

A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication.

The need for non-interactive human recognition systems to ensure safe isolation between users and biometric equipment has been exposed by the COVID-19 pandemic. This study introduces a novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication (MSDCS-PHGA). The proposed MSDCS-PHGA involves segmenting, preprocessing, and resizing silhouette images into three scales. Gait features are extracted from these multi-scale images using custom convolutional layers and fused to form an integrated feature set. This multi-scaled deep convolutional approach demonstrates its efficacy in gait recognition by significantly enhancing accuracy. The proposed convolutional neural network (CNN) architecture is assessed using three benchmark datasets: CASIA, OU-ISIR, and OU-MVLP. Moreover, the proposed model is evaluated against other pre-trained models using key performance metrics such as precision, accuracy, sensitivity, specificity, and training time. The results indicate that the proposed deep CNN model outperforms existing models focused on human gait. Notably, it achieves an accuracy of approximately 99.9% for both the CASIA and OU-ISIR datasets and 99.8% for the OU-MVLP dataset while maintaining a minimal training time of around 3 min.

A NOVEL MULTIMODAL BIOMETRIC SYSTEM BASED ON DEEP FUSION OF ECG AND EAR

Due to their unique and measurable properties, biometric security systems are more reliable and secure than traditional ones. However, unimodal biometric systems suffer from various problems such as spoof attacks, non-universality, intra-class variances, inter-class similarities, and noisy data. To overcome these problems, multimodal biometric systems which utilize more trait features have emerged to efficiently authenticate the identity of the individuals in various real-world applications. Along the same line, this paper proposes a multimodal biometric system for human recognition based on deep features fusion of electrocardiograms (ECG) signals and ear images. The proposed system is hard to spoof compared to current systems as the ear biometric provides a fixed structure over an acceptable period of human life, and the ECG offers the characteristic of the person's liveness. It also applies a transfer-learning methodology to extract discriminative deep features by exploiting a pre-trained VGG-m Net model. Furthermore, to improve the efficiency of the proposed model’s training, augmentation techniques were utilized to further increase the size of the training data. A course of experiments has been conducted to assess the performance of the proposed approach for unimodal and multimodal biometric traits. The experimental results reveal that the proposed system achieves promising results and outperforms the unimodal of ECG and ear, and other state-of-the-art multimodal biometric systems.

WIFI-Based Human Identification of gait recognition in muti-scenario

With the development of 5G and the maturity of embedded technology, the Internet of Things has become the most promising technology at present, and human motion recognition and fingerprint feature recognition are hot research topics in the Internet of Things. At the same time, the way of human-computer interaction no longer satisfies the interaction only through screens and our computing devices. We hope to achieve human-computer interaction through simpler and more direct operations. For example, gestures, speech, etc., while the current methods of human recognition are mostly achieved through video or wearable devices, both of which have certain limitations. For example, when using video devices to recognize human bodies, it is necessary to ensure that there are no obstacles on the line of sight (LOS) in the video and sufficient ambient light is available. The Wi-Fi recognition method proposed in this article has the advantages of non-wearable, no light source restriction, and the ability to achieve human recognition using non-line-of-sight paths. Wi-Fi is ubiquitous in modern society, so using Wi-Fi signals for indoor human sensing has important research value. Based on existing Wi-Fi gait recognition work, this paper proposes a Wi-Fi-RSMID system for indoor human recognition. By analyzing the channel state information of Wi-Fi signals during human walking, the Wi-Fi-RSMID system extracts key feature information through PCA principal component analysis of thirty subcarriers of CSI. It collects 155 feature points from five principal components of PAC using a method that combines time-frequency domain information, and achieves human recognition through random subspace method. Experiments show that the system can effectively identify the identities of 15 people in different scenarios, with an average recognition rate of about 75.3% - 85.6%.

Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Human recognition system through major and minor finger knuckle pattern fusion by symmetric-sum method

Real-Time Face Recognition System with Enhanced Security Features using Deep Learning


 
 
 
 Abstract: Identification of people and mask detection has long been a captivating topic, in terms of research and business. This topic has received increasing attention in recent phases due to the speedy advancement of Artificial Intelligence (AI). Nowadays, a lot of applications, including phone unlocking systems, criminal identification systems, and even home security systems, use face recognition as a common technique. Due to the fact that this method only requires a facial image instead of other dependencies like a key or card, it is more secure. Face detection and face identification are often the first two elements of a human recognition system. Even during COVID-19, it is considered the best way to stop the spread of the COVID-19 virus is by wearing a face mask. The risk of contracting the virus can be reduced by almost 70% only by wearing a face mask. In order to promote community health. This Study aims to produce a highly precise and real-time method that can effectively recognize people and identify non-mask faces in public. When a person stands in front of the device, this application detects the human face automatically using detection, extraction, and recognition algorithms. The proposed work applies the Viola-Jones algorithm for face recognition and the YOLOv5 algorithm for mask detection and classification. When the proposed work is tested, this shows higher accuracy in mask detection which is 92.8%.
 
 
 

Cancelable scheme for bimodal biometric authentication

The use of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially over traditional authentication systems in recent years. Multimodal BAS was introduced to solve unimodal BAS’s difficulties and improve security. Privacy and security are two significant concerns to be addressed in BAS, as biometric traits are irrevocable. Researchers employed cancelable biometrics in the past few years to propose several privacy-preserving BAS. We propose a privacy-preserving bimodal cancelable BAS (PPBCBAS) to overcome these problems. The traits used in our method are iris and fingerprint. Features are extracted from both the traits, and feature level fusion is done by concatenating the feature vectors of iris and fingerprint. PPBCBAS uses a quotient filter to generate the cancelable template, and the comparison is made on these transformed templates using the modified Hamming distance. PPBCBAS has been tested on three publicly available databases to analyze its efficiency. PPBCBAS satisfies the diversity, irreversibility, and revocability properties and achieves decent performance.

IS-WARS: Intelligent and Stealthy Adversarial Attack to Wi-Fi-Based Human Activity Recognition Systems

The non-intrusive human activity recognition has been envisioned as a key enabler for many emerging applications requiring interactions between humans and computing systems. To accurately recognize different human behaviors, ubiquitous wireless signals are widely adopted, e.g., Wi-Fi signals, whose Channel State Information (CSI) can precisely reflect human movements. Unfortunately, nearly all Wi-Fi-based recognition systems assume a clean wireless environment, i.e., no interference will compromise the developed algorithms, which, apparently, is not feasible in practice. Even worse, for systems using Wi-Fi 2.4GHz signals, the widely existing interference from coexisting protocols, such as ZigBee, Bluetooth, and LTE-Unlicensed, can easily compromise the recognition process, posing a hard limit on further enhancing the accuracy. Therefore, this work uncovers a new signal adversarial attack against Wi-Fi-based human activity recognition systems, by intentionally injecting interference using coexisting protocol signals. The contaminated Wi-Fi signal will distort CSI estimation and finally output a false recognition result. Different from traditional jamming attacks, this new adversarial attack is intelligent and stealthy in terms of avoiding being detected from traffic analysis. Along with both theoretical analysis and extensive real-world experiments, we have shown this newly-identified attack can easily compromise many existing Wi-Fi-based human recognition systems while still bypassing existing schemes for malicious signal detection.

EB: Eye Biometrics Based a Novel Human Recognition System for Cardless Online Payment Security Improvement in ATMs

Here we are introducing a novel approach for enhancing the security of traditional transactions of ATM .Individual confirmation is quite possibly the main ways to deal with work on the security. Be that as it may, the conventional individual validation techniques or advance hybrid. Biometrics, which naturally utilizes the physiological or social quality of individuals to perceive their characters, is one of the powerful procedures to defeat these issues. Biometrics is a field of programmed individual ID dependent on physiological and social attributes of people. A conduct trademark is progressively an impression of an individual’s physiological cosmetics Validations. We used Eye Biometric Recognition System using OpenCV for cardless transaction & Identity Verification after ATM Transactions. We proposed EB verification model in the research for final compilation of transaction. Though it may require more time for verification but security is prior to time & cyber thefts. The efficiency of Software we Proposed came out to be 98.52%, 95.75%, 98.86% while performing an extensive testing of our algorithm with 3 datasets named UBIRIS.V1, UBIRIS.V2, IITD and the algorithms we have used in OpenCV For feature detection & extraction are ORB, brute force algorithms. With the hope of Interest in youth and additional feature of security to be embedded in online transaction is the motivation of the research.

Neural Network-Based Fingerprint Matching and Human Recognition System

Neural Network-Based Fingerprint Matching and Human Recognition System

Sensor and Attitude Analysis of Track and Field Training Action Recognition Based on Artificial Intelligence

In order to provide effective information support to athletics training and to increase the effectiveness of athletics training, in-depth analysis of motion recognition sensors and attitudes based on artificial intelligence was conducted. First of all, the basic conditions of type analysis, cognitive technology, and the current situation were studied, and the basic theory related to it was studied, and on this basis, a human position analysis and recognition system based on artificial intelligence movement training sensors was developed. We studied the technology in depth. Experiments have shown that the approach data collected by the system’s inertia node is transmitted wirelessly to the computer-side software to restore the trend and identify each trend and parameter with high accuracy. During the 30-minute test, the static error was within 1° and the dynamic error was within 5°, which is acceptable and adheres well to dynamic conditions. The system can overcome the limitations of traditional wired or optical methods and be widely used in sports training, human-computer interaction monitoring, rehabilitation medicine, games, film, and television production.

Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

Emotions are an essential part of a person’s mental state and influence her/his behavior accordingly. Consequently, emotion recognition and assessment can play an important role in supporting people with ambient assistance systems or clinical treatments. Automation of human emotion recognition and emotion-aware recommender systems are therefore increasingly being researched. In this paper, we first consider the essential aspects of human emotional functioning from the perspective of cognitive psychology and, based on this, we analyze the state of the art in the whole field of work and research to which automated emotion recognition belongs. In this way, we want to complement the already published surveys, which usually refer to only one aspect, with an overall overview of the languages ontologies, datasets, and systems/interfaces to be found in this area. We briefly introduce each of these subsections and discuss related approaches regarding methodology, technology, and publicly accessible artefacts. This comes with an update to recent findings that could not yet be taken into account in previous surveys. The paper is based on an extensive literature search and analysis, in which we also made a particular effort to locate relevant surveys and reviews. The paper closes with a summary of the results and an outlook on open research questions.

Design and Exploration of College English Reading, Writing, and Translation Teaching Classroom Based on Machine Learning

The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normalization optimization, and their optimal hyperparameters are analyzed. Consequently, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization, and the polynomial kernel-based SVM algorithm is proved to present the most remarkable improvement and accuracy after optimization, which is as high as 95.23% or 66.96% improvement. Therefore, the polynomial kernel-based SVM algorithm is chosen for the college English (R-W-T) classroom-oriented human pose recognition (HPR) system. Thus, English teachers can better grasp the students’ psychological state and classroom atmosphere and ameliorate unsatisfactory teacher-student interaction in students’ English (R-W-T). The proposal plays a positive role in cultivating college students’ strong (R-W-T) EPL, which is of great significance in improving the English classroom.

Clothing attribute recognition via a holistic relation network

Clothing attribute prediction is a fundamental image classification task in the field of computer vision. Motivated by the human recognition system, we investigate the task relationship and spatial importance where people usually utilize these useful clues to assist in recognizing clothing attributes. In this paper, we propose a novel Holistic Relation Network (HRNet) for clothing attribute recognition, considering the fusion of multiple relations, including spatial and spatial relation via a spatial relation module, spatial and task relation via a task attention module, and task and task relation via a graph context reasoning module. Specifically, we first use the backbone network to extract features from the input image, two types of attention models followed will further learn the features, then the graph context reasoning module will be used to further enhance the features, and finally, a classifier exploited to classify the clothing attributes with the learned representation information. Without using manual image feature filtering methods, this paper aims to achieve clothing attribute recognition by deeply exploring the relationships among different clothing attribute recognition tasks. In this paper, we use double-branches of the attention model to model the relevance of spatial context information and learn more discriminative feature representations from multitask features for clothing attribute prediction. Derived from the prior knowledge learned from the two above-mentioned attention models, we further propose a graph-relation model constructing relationships among different clothing attribute tasks by integrating the spatial association relationships among multitask. The proposed HRNet only uses image-level annotation but it owns a good ability for obtaining distinguishing feature representations. We obtain state-of-the-art performance, which is demonstrated by extensive experiments on three mainstream benchmarks, for example, woman clothing data set, man clothing data set, and shop-domain clothing data set.

A competent multimodal recognition using imperfect region based face and gait cues using Median-LBPF and Median-LBPG based PCA followed by LDA

Today, all conventional computer based multimodal biometric system for human recognition that combines face and gait cues have primarily focused on recognition from perfect face and gait images. There are circumstances in which perfect face and gait images may not be obtainable which means probe images are imperfect. This paper proposes new methods Median Local Binary Pattern of Face image (Median-LBPF) and Gait image (Median-LBPG) to extract the features of imperfect face and gait images efficiently for better recognition. Initially the given imperfect face and gait images are divided into six overlapped regions called top, bottom, left, right, vertical center, horizontally center overlapped half images. The features of these six overlapped regions of imperfect face and gait images in the spatial domain are extracted by using Median-LBPF and Median-LBPG. Subsequently the dimensionality of the feature sets are reduced by a two stage feature reduction algorithms Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA). Next for classification, Euclidean distance measure is used to calculate the minimum of minimum distance between the six overlapped regions of given imperfect face and gait probe images and the corresponding regions of all six overlapped regions in the training sets separately called face decision and gait decision. Finally the face decision and gait decision are fused at decision level for recognition. The proposed methods are tested by using publically available data sets ORL face and CASIA gait. The experimental results show that features of a region of face and gait images are adequate for recognition and its average recognition performance is same as perfect face and gait images.

A vision-based human-robot collaborative system for digital twin

Flexible and safe human-robot collaboration depends on accurately capturing the three-dimensional motion of humans and robots in the field of smart manufacturing. In this paper, a novel approach to developing a human-robot collaborative assembly system is proposed and applied to the field of digital twins. Within the context, a deep learning-based model is explored to develop a depth camera-based human recognition system for accurate prediction of key points for human skeletons model and high-precision human localisation in a human-robot collaborative setting. After the functional mapping of robot calibration, a collision warning module leverages coordinates of key human-robot points to facilitate efficient and safe human-robot collaborative assembly.

Researching a machine learning algorithm for a face recognition system

This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence.
 The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face.
 The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition.
 A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented.
 The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence.
 During the study, the following tasks were solved:
 – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials);
 – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points;
 – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code;
 – development of a match search module, that is, the module compares the faceprint with the database until a match is found

STUDY AND ANALYSIS OF CLASSIFIERS FOR USE IN EMOTION RECOGNITION

Today, in the process of obtaining education, and not only, there is a widespread use of the method of communication through online platforms. The massive use of online platforms for communication creates the need for human emotion recognition systems. In facial recognition systems and human emotions, information is collected, analyzed and processed using special methods and learning algorithms. The paper considers and analyzes classifiers for face recognition, such as the HAAR classifier, the feature extraction method, local binary histogram (LBP), neural convolutional networks (CNN), which contribute to the effective implementation of emotion recognition methods.

Multimodal CSI-Based Human Activity Recognition Using GANs

Channel state information (CSI)-based human activity recognition (HAR) has received great attention in recent years due to its advantages in privacy protection, insensitivity to illumination, and no requirement for wearable devices. In this article, we propose a multimodal channel state information-based activity recognition (MCBAR) system that leverages existing WiFi infrastructures and monitors human activities from CSI measurements. MCBAR aims to address the performances degradation of WiFi-based human recognition systems due to environmental dynamics. Specifically, we address the issue of nonuniformly distributed unlabeled data with rarely performed activities by taking advantages of the generative adversarial network (GAN) and semisupervised learning. We apply a multimodal generator to approximate the CSI data distribution in different environment settings with limited measured CSI data. The generated CSI data using the multimodal generator can provide better diversity for knowledge transfer. This multimodal generator improves the ability of MCBAR to recognize specific activities with various CSI patterns caused by environmental dynamics. Compared to state-of-the-art CSI-based recognition systems, MCBAR is more robust as it is able to handle the nonuniformly distributed CSI data collected from a new environment setting. In addition, diverse generated data from the multimodal generator improves the stability of the system. We have tested MCBAR under multiple experimental settings at different places. The experimental results demonstrate that our algorithm overcomes environmental dynamics and outperforms existing HAR systems.

Facial Feature Extraction for Emotion Classification Using Fuzzy C-Mean Clustering

Background: Automatic human emotion recognition system is an active area of research due to its wide applications in the field of Human Computer Interaction(HCI) systems, driver fatigue monitoring systems, surveillance systems, human assistance systems, smile detectors etc. Objective: The study presents a fuzzy based approach to extract facial features from input image and builds different classification models to classify the image into two emotion classes i.e. happy and neutral. The system has potential implications in the field of smile detection systems, customer experience analysis and patient monitoring systems. Methods: The proposed system determines the dimensional attributes (l-attribute and w-attribute) of mouth region extracted from the facial image using viola-jones algortithm. The feature set is generated by using a total of 136 images from JAFFE, NimStim and MUG dataset. The differentiating power of the attribures is then evaluated using five different classification models. Results: The accuracy, precision and recall is determined for each classification model. The results show good accuracy of 70% for the grayscale JAFFE and NimStim databases and 95% for the coloured MUG database. Conclusion: The mouth features calculated in the study are based on the geometric coordinates which eliminates the possibility of false distance measurements due to presence of noise or shadows.

Development and Progress in Sensors and Technologies for Human Emotion Recognition.

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.