Human Recognition Method Research Articles

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%.

Biometric privacy protection: What is this thing called privacy?

AbstractWe are at the wake of an epochal revolution, the Information Revolution. The Information Revolution has been accompanied by the rise of a new commodity, digital data, which is changing the world including methods for human recognition. Biometric systems are the recognition technology of the new age. So, privacy scholars tend to frame biometric privacy protection chiefly in terms of biometric data protection. The author argues that this is a misleading perspective. Biometric data protection is an extremely relevant legal and commercial issue but has little to do with privacy. The notion of privacy, understood as a personal intimate sphere, is hardly related to what is contained in this private realm (data or whatever else), rather it is related to the very existence of a secluded space. Privacy relies on having the possibility to hide rather than in hiding anything. What really matters is the existence of a private sphere rather than what is inside. This also holds true for biometric privacy. Biometric privacy protection should focus on bodily and psychological integrity, preventing those technology conditions and operating practices that may lead to turn biometric recognition into a humiliating experience for the individual.

Ontology of human identification by face and body motions in video surveillance systems

At the present stage of advancing information technology, the development of models and recognition methods by body movements and faces in video surveillance systems is a topical problem. This task is essential for security issues, especially at facilities with mass gatherings to counter a terrorism-related crime. The paper presents a classification of the main biometric features and parameters that characterize a potential violator. This classification has been developed for security control systems and access systems of enterprises. A block diagram of merging biometric data and violator recognition by body motions and face which can be used as the basis for the development of security control systems is proposed. The types of systems and methods of human recognition by body movements and face are considered, their advantages and disadvantages are revealed. It is noted that for accurate violator recognition under a set of biometric features, it is reasonable to use a combination of recognition methods which will allow to make the right decisions regarding the identification of a potential violator. This paper attempts to consider the main aspects related to human recognition by body movements and face in video surveillance in general, in contrast to well-known works devoted to individual biometric features.

A review on face mask detection using image processing

Face identification becomes an extremely challenging task, while wearing masks since some aspects of the face are obscured, making it crucial to develop advanced facial recognition technologies that can handle these complex scenarios effectively. The purpose of this paper is to offer an overview of several different approaches and algorithms that are utilized for human recognition when a face mask is applied. In this study, a number of distinct methodologies, including the Haar cascade, Adaboost, VGG-16 CNN Model, and others, are discussed. These approaches are compared side by side in order to determine which one has the best chance of success. It is possible that in the not too distant future, with the continued development of technology and the passage of time, more trustworthy methods for human recognition while wearing a face mask will come into use. It describes some of the practical uses of face detection and highlights the potential for advancements in reliable methods for human recognition even when individuals are wearing face masks. This technology has a variety of uses in public areas, schools, and other settings where persons need to be recognized despite wearing face masks in order to assist society. Facial recognition technology can also enhance security and streamline access control processes in a wide range of applications, from airports to corporate offices.

Lip print‐based identification using traditional and deep learning

The concept of biometric identification is centred around the theory that every individual is unique and has distinct characteristics. Various metrics such as fingerprint, face, iris, or retina are adopted for this purpose. Nonetheless, new alternatives are needed to establish the identity of individuals on occasions where the above techniques are unavailable. One emerging method of human recognition is lip-based identification. It can be treated as a new kind of biometric measure. The patterns found on the human lip are permanent unless subjected to alternations or trauma. Therefore, lip prints can serve the purpose of confirming an individual's identity. The main objective of this work is to design experiments using computer vision methods that can recognise an individual solely based on their lip prints. This article compares traditional and deep learning computer vision methods and how they perform on a common dataset for lip-based identification. The first pipeline is a traditional method with Speeded Up Robust Features with either an SVM or K-NN machine learning classifier, which achieved an accuracy of 95.45% and 94.31%, respectively. A second pipeline compares the performance of the VGG16 and VGG19 deep learning architectures. This approach obtained an accuracy of 91.53% and 93.22%, respectively.

A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination

Many studies have been actively conducted on human recognition in indoor and outdoor environments. This is because human recognition methods in such environments are closely related to everyday life situations. Besides, these methods can be applied for finding missing children and identifying criminals. Methods for human recognition in indoor and outdoor environments can be classified into three categories: face-, body-, and gait-based methods. There are various factors that hinder indoor and outdoor human recognition, for example, blurring of captured images, cutoff in images due to the camera angle, and poor recognition in images acquired in low-illumination environments. Previous studies conducted to solve these problems focused on facial recognition only. This is because the face is typically assumed to contain more important information for human recognition than the body. However, when a human face captured by a distant camera is small, or even impossible to identify with the naked eye, the body’s information can help with recognition. For this reason, this survey paper reviews both face- and body-based human recognition methods. In previous surveys, recognition on low-resolution images were reviewed. However, survey papers on blurred images are not comprehensive. Therefore, in this paper, we review studies on blurred image restoration in detail by classifying them based on whether deep learning was used and whether the human face and body were combined. Although previous survey papers on recognition covered low-illumination environments as well, they excluded deep learning methods. Therefore, in this survey, we also include details on deep-learning-based low-illumination image recognition methods. We aim to help researchers who will study related fields in the future.

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy.

Human Recognition Using Joint Coordinate Images (JCIs) with Convolutional Neural Network

Human recognition with skeletal data has the advantage in detecting people without their face characteristic on image. However, the accuracy of recognition by this method is always low because it relies deeply on manual feature selection. We propose a novel human recognition method called Joint Coordinate Images (JCIs) with Convolutional Neural Network (CNN) based on the image generated from skeletal information tracked by KinectV1. In order to represent human physical skeletal characteristic, the coordinate values XYZ of human joint tracked by KinectV1 are firstly created in color image called Joint Coordinate Images (JCIs), in which the relative position of the pixels represents the skeletal structure characteristics of participants with shape in “大” structure. Secondly, a new convolution neural network classifier Lenet-5 model, which always performed well in image classification, was modified to be able to input our JCIs for human recognition. The experimental results show that human recognition using joint coordinate image and Lenet-5 network can reach the highest recognition accuracy of 90.00% on the G3D dataset, which demonstrates the feasibility to transform the skeletal coordinate information into color image for human recognition task and could be used as a complementary method to the well-known application of face recognition.

Proposed Method for Face Image Recognition Using Spectral Eigenvector Algorithms

Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. Biometric systems Though Iris Scan, Finger Print and Hand Geometry biometric system have proven to be effective, it requires cooperation from the person being scanned, the last are critical in a wide range of applications such as banking system, E-commerce, smart cards, and access control to secure system, face recognition system is one of the most reliable biometric systems, which is used for identifying persons. The proposed system describes a method for human recognition based on eigenface, correlation distance, training set of images, and a threshold which it used to classify the images and accept the detection of images that have a minimum value below the threshold. And noise removal used to enhance the performance of the original PCA algorithm in the recognition process; it gives more accurate results in recognition. This study aim is to design a face image recognition system, which is capable of identifying a face with high level of accuracy. Therefore, this system can be applied to a wide range of many applications.

Recognition of surgically altered face images: an empirical analysis on recent advances

Biometric recognition plays a vital role in our daily lives. Face recognition is a subset of biometric recognition. Face verification and identification processes are prone to plastic surgery challenges which are commonly used nowadays to alter facial features for good looking demonstration. With increasing trend in technology and intellect robust biometric recognition systems are developed for human recognition after plastic surgery. However, these systems have some limitations because recognition after plastic surgery is affected by lightning, aging, pose, expressions, disguise and occlusion effects. In this survey, we aim to highlight the mitigating effects of cutting edge plastic surgical operations. These procedures lead to medical identity thefts, which is a serious offense for human community as an individual’s identity is forged. Thus, this makes one’s safety a critical issue and human recognition after plastic surgery a crucial challenge. Since the existing methods for human recognition after plastic surgical operations are not promising, in the current scenario plastic surgical operations secure above facial recognition. A number of existing biometric recognition algorithms for face images have been opted such as principal component analysis, Fisher/linear discriminant analysis, local feature analysis, local/circular binary patterns, speeded up robust features, granular system, correlation based approach, evolutionary granular/genetic approach, grouping recognition by parts and sparse demonstration approach, geometrical face recognition after plastic surgery, feature/texture based fusion scheme and deep convolutional neural networks (DCNN). The validation metrics used for the evaluation of recognition techniques are expected error rate, recognition rate, half total error rate and F-score. All algorithms are tested on an open plastic surgery facial dataset containing 1800 before and after surgery image samples pertaining to 900 humans. For a particular human being, two front facing image samples with appropriate luminance and unbiased gesture are taken: the former is taken pre cosmetic procedure and the latter is taken post cosmetic procedure. It has been deduced that feature and texture based fusion approach gives best results till date. It is predicted that DCNN has full potential of giving consistent results on surgical databases as it is already validated on non surgical databases. The need of a novel human identification system which is steady to the anomalies posed by plastic surgical operations is highlighted in this survey.

CNN-Based Multimodal Human Recognition in Surveillance Environments.

In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research being performed on indoor human recognition. Previous research on indoor recognition has mainly focused on face recognition because the camera is usually closer to a person in an indoor environment than an outdoor environment. However, due to the nature of indoor surveillance cameras, which are installed near the ceiling and capture images from above in a downward direction, people do not look directly at the cameras in most cases. Thus, it is often difficult to capture front face images, and when this is the case, facial recognition accuracy is greatly reduced. To overcome this problem, we can consider using the face and body for human recognition. However, when images are captured by indoor cameras rather than outdoor cameras, in many cases only part of the target body is included in the camera viewing angle and only part of the body is captured, which reduces the accuracy of human recognition. To address all of these problems, this paper proposes a multimodal human recognition method that uses both the face and body and is based on a deep convolutional neural network (CNN). Specifically, to solve the problem of not capturing part of the body, the results of recognizing the face and body through separate CNNs of VGG Face-16 and ResNet-50 are combined based on the score-level fusion by Weighted Sum rule to improve recognition performance. The results of experiments conducted using the custom-made Dongguk face and body database (DFB-DB1) and the open ChokePoint database demonstrate that the method proposed in this study achieves high recognition accuracy (the equal error rates of 1.52% and 0.58%, respectively) in comparison to face or body single modality-based recognition and other methods used in previous studies.

3D ear recognition using global and local features

This paper proposes a method for human recognition based on 3D ears and makes two important contributions. First, it proposes a global 3D descriptor, and second, it proposes a strategy to combine local and global descriptors for superior recognition performance. The proposed global descriptor is computed as follows. Spheres of different radii are placed at each point p in the point cloud, considering it as a center. A histogram is computed from the number of points falling in the annular regions of the concentric spheres. By utilizing the histograms of point p and its neighbors in a ring of fixed radius, an encoded value is obtained and is used to construct a coded image. This image is further divided into blocks which are subsequently binned to a histogram. Finally, the global descriptor is computed by concatenating all the histograms. The combined representation of four popular local 3D descriptors and the proposed global descriptor, yields superior results as compared to the case when local and global descriptors are used alone. The proposed technique has been validated on University of Notre Dame (UND), collection-J2 database and on our in-house database and has achieved a rank-1 recognition rate of 98.69% and 98.90%, respectively.

Fuzzy Analytic Hierarchy Process based Template Data Analysis of Multimodal Biometric Conceptual Designs

The Biometric Systems are automatic tools, which are used to provide authentication during various applications of modern computing. Traditional methods of human recognition because of their limitations are being replaced by biometric applications. Fingerprint biometric system is one of the most widely used system while Hand geometry is comparatively less popular because of its few limitations. Multimodal biometric systems are developed to address few of the limitations of unibiometric systems and works on the principle of using more than one biometric trait for human recognition. In this paper, three conceptual designs for bimodal systems using two important modalities have been presented. A Template security analysis of all the designs is performed using Fuzzy based Analytic Hierarchy Process (FAHP) with five decision parameters. Result indicates that bimodal conceptual design-2 is optimal among all the three in terms of various decision parameters. Template may be well secured at feature level fusion of a multimodal biometric system and results into overall cost effective design. It is also noticed that template security before storage into database is an important parameter for effective and efficient design of a multimodal biometric system.

Human Detection and Face Recognition Using 3D Structure of Head and Face Surfaces Detected by RGB-D Sensor

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/11.jpg"" width=""300"" /> Summary of proposed method</div>Home service robots must possess the ability to communicate with humans, for which human detection and recognition methods are particularly important. This paper proposes methods for human detection and face recognition that are based on image processing, and are suitable for home service robots. For the human detection method, we combine the method proposed by Xia et al. based on the use of head shape with the results of region segmentation based on depth information, and use the positional relations of the detected points. We obtained a detection rate of 98.1% when the method was evaluated for various postures and facing directions. We demonstrate the robustness of the proposed method against postural changes such as stretching the arms, resting the chin on one’s hands, and drinking beverages. For the human recognition method, we combine the elastic bunch graph matching method proposed by Wiskott et al. with Face Tracking SDK to extract facial feature points, and use the 3D information in the deformation computation; we obtained a recognition rate of 93.6% during evaluation.

Human Recognition Methods based on Biometric Technologies

has revolutionized the Human Recognition Technology .The base and reach of human recognition system has been expanded by the innovative uses of Biometric devices .Human recognition have becomes one of the most well-off fields in the past ten years. Biometrics is the science of establishing the identity of an individual based on physical, chemical or behavioural attributes of the person; it deals with the automated recognition of individuals based on biological and behavioural characteristics. Biometrics spread its wings in a wide range of applications and found itself as a reliable source in fields such as electronic data security, ecommerce, internet access, physical access control, PDA, Government applications such as national ID card, social security, welfare- disbursement, border control, military surveillance, etc As the application areas are emerging, the implementation of biometric systems in both commercial and government sectors is increasing and therefore leading to enormous security breaches in the installed systems. Currently, the most effective means of Human Recognition is to use biometric system. So while designing biometric system, security of the system is one of the factors which have to be considered along with the increasing performance at reasonable costs. The objective of this paper is to explore the potential of fast developing Biometric systems such as fingerprints, face recognition, iris recognition for human identification. This paper discusses the main features of the biometric system: architecture, evaluation methodology used in these system and also various issues related to security of the biometric system.

Human Recognition System using Cepstral Information

This paper presents a new method for human recognition using the cepstral information. The proposed method consists in extracting the Linear Frequency Cepstral Coefficients (LFCC) from each heartbeat in the homomorphic domain. Thus, the Hidden Markov Model (HMM) under Hidden Markov Model Toolkit (HTK) is used for electrocardiogram (ECG) classification. To evaluate the performance of the classifier, the number of coefficients and the number of frequency bands are varied. Concerning the HMM topology, the number of Gaussians and states are also varied. The best rate is obtained with 32 coefficients, 24 frequency bands, 1 Gaussian and 5 states. Further, the method is improved by adding dynamic features: the first order delta (?) and energy (E) to the coefficients. The approach is evaluated on 18 healthy signals of the MIT_BIH database. The obtained results reveal which LFCC with energy that make a 33 dimensional feature vector leads to the best human recognition rate which is 99.33%.

Human recognition based on retinal images and using new similarity function

This paper presents a new human recognition method based on features extracted from retinal images. The proposed method is composed of some steps including feature extraction, phase correlation technique, and feature matching for recognition. In the proposed method, Harris corner detector is used for feature extraction. Then, phase correlation technique is applied to estimate the rotation angle of head or eye movement in front of a retina fundus camera. Finally, a new similarity function is used to compute the similarity between features of different retina images. Experimental results on a database, including 480 retinal images obtained from 40 subjects of DRIVE dataset and 40 subjects from STARE dataset, demonstrated an average true recognition accuracy rate equal to 100% for the proposed method. The success rate and number of images used in the proposed method show the effectiveness of the proposed method in comparison to the counterpart methods.

2P1-P15 効率的なインタラクションによる複雑環境下でのユーザ探索(コミュニケーション・ロボット)

In order to find out a particular user under complex environments, the robot can make full use of the information of interaction. Especially in the case that the robot has no information about the user on ahead, the method of human recognition or speech recognition can't be used. The way of using efficient interaction to look for the user will be more effective. Let the robot try to talk to the user, and detect the changes (both visual and auditory information) of the environment when the user makes a response. By repeating this process several times, the user can be finally fixed. Furthermore, this method can handle any complex environment by making some special requirements to the people around the robot, e.g. to request noisy people to be quiet.

Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor

Human object recognition and tracking is important in robotics and automation. The Kinect sensor and its SDK have provided a reliable human tracking solution where a constant line of sight is maintained. However, if the human object is lost from sight during the tracking, the existing method cannot recover and resume tracking the previous object correctly. In this paper, a human recognition method is developed based on colour and depth information that is provided from any RGB-D sensor. In particular, the method firstly introduces a mask based on the depth information of the sensor to segment the shirt from the image (shirt segmentation); it then extracts the colour information of the shirt for recognition (shirt recognition). As the shirt segmentation is only based on depth information, it is light invariant compared to colour-based segmentation methods. The proposed colour recognition method introduces a confidence-based ruling method to classify matches. The proposed shirt segmentation and colour recognition method is tested using a variety of shirts with the tracked human at standstill or moving in varying lighting conditions. Experiments show that the method can recognize shirts of varying colours and patterns robustly.

Gait recognition method based on kernel principal component analysis

Concerning the issue of extracting features more efficiently from a sequence of gait frames and real-time recognition,an effective human recognition method based on Mean Gait Energy Image(MGEI) was described,which utilized Kernel Principal Component Analysis(KPCA).A pre-processing technique was used to segment the moving silhouette from the walking figure.The algorithm obtained the gait quasi-periodicity through analyzing the width information of the lower limbs' gait contour edge,and the MGEI was calculated from gait period.KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images.In this paper,KPCA could make use of the high correlation between different MGEIs for feature extraction by selecting the proper kernel function,and Euclidean distance weighted by variance reciprocal was designed as the classifier.The experimental results show that the proposed approach has better recognition performance and the computation time is greatly reduced.