Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
G. Merlin Linda ◽  
N.V.S. Sree Rathna Lakshmi ◽  
N. Senthil Murugan ◽  
Rajendra Prasad Mahapatra ◽  
V. Muthukumaran ◽  
...  
Keyword(s):  
Spatial Information ◽  
Intelligent System ◽  
Gait Recognition ◽  
Research Work ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Content Type ◽  
Surveillance Videos ◽  
Sensor Devices ◽  
Intelligent Recognition

PurposeThe paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.Design/methodology/approachThis proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.FindingsThis research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.Research limitations/implicationsThe proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Practical implicationsThis research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.Originality/valueThis proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Admir Barolli ◽  
Shinji Sakamoto
Keyword(s):  
Intelligent System ◽  
Research Work ◽  
Simulation System ◽  
Web Interface ◽  
Placement Problem ◽  
Content Type ◽  
Node Placement ◽  
Hybrid Intelligent System ◽  
Intelligent Simulation ◽  
Mesh Clients

Purpose The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs. Design/methodology/approach The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA. Findings This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions. Research limitations/implications For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered. Originality/value This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

Development of intelligent system for predicting MTR in EDC process using powder metallurgical tools

2020 ◽  
Vol 10 (3) ◽  
pp. 321-337
Author(s):  
Eswara Krishna Mussada ◽  
P.K. Patowari
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Goodness Of Fit ◽  
Intelligent System ◽  
Research Work ◽  
Fuzzy Model ◽  
Dielectric Fluid ◽  
Material Transfer ◽  
Content Type ◽  
Work Material

PurposeThe current research work presents the application of fuzzy logic modeling for electric discharge coating (EDC) process for predicting the material transfer rate (MTR), which has the capability of producing thick and thin films on the conductive substrate material.Design/methodology/approachThirty-two real-time experiments were conducted, and fuzzy rules were framed from the inference made from this experimental data. Validating experiments were carried out to check the feasibility of the developed model in prediction.FindingsA fair agreement has been observed between experimental results and the outcomes of fuzzy model. This was supported by a goodness of fit value of 0.917. The values of adjusted R2 and standard error were 0.914 and 19.112, respectively.Research limitations/implicationsCurrent research deals with the prediction of MTR at various parameter grouping conditions, which majorly influence the response parameters. However, other parameters such as quality of the dielectric fluid, flushing pressure and purity of the electrode and work material and so on that influence the response parameters could be further investigated and stand as a future scope of the current work.Practical implicationsMTR is a response parameter that accounts the actual material transfer to the workpiece during the deposition process. This parameter supports/alters the hardness, adhesion, wear resistance and other mechanical properties of the work material. The current modeling work helps to take an optimum decision without conducting large number of experiments at the industrial scale. Due to the nature of fuzzy logic, this method has a potential advantage in dealing with real-time data for various industrial applications.Originality/valueDeveloping a fuzzy model for EDC process is not yet addressed, and to attain the economic objective of the process, optimal deposition conditions must be determined, which help the industries to reduce the operation costs. The current study outcomes substantiate the effectiveness of the fuzzy logic in decision-making and prediction of response parameters.

A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices

2019 ◽  
Vol 8 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Sampath Dakshina Murthy Achanta ◽  
Karthikeyan T. ◽  
Vinoth Kanna R.
Keyword(s):  
Gait Analysis ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Research Work ◽  
Biomechanical Model ◽  
Communication Process ◽  
Analysis Model ◽  
Content Type ◽  
Body Sensors ◽  
Physically Challenged

Purpose The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues. Design/methodology/approach Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors. Findings Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values. Originality/value The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.

A survey of R&D of intelligent STR system based on behavior pattern recognition in China

2016 ◽  
Vol 19 (2) ◽  
pp. 109-121 ◽  
Author(s):  
Jun Tang
Keyword(s):  
Pattern Recognition ◽  
Intelligent System ◽  
Behavior Pattern ◽  
Recognition System ◽  
Decision Procedures ◽  
Customer Behavior ◽  
Content Type ◽  
The Core ◽  
Core Elements ◽  
Str System

Purpose The purpose of this paper is to systematically study the research and development history of suspicious transaction reporting (STR) system in China, and introduce the core elements in constructing an intelligent surveillance system which could provide a solution to the situation of low effectiveness and efficiency in Chinese Financial Institutions (FIs) STR procedure nowadays. The solution outputs those falling out of the normal customer behavior profiles instead of only extracting data by the rules issued by authorities. Design/methodology/approach This paper reviews the latest literature, regulations and guidelines of STR gathered domestically and overseas, and hands out questionnaire surveys to hundreds of software vendors, regulators and FIs, details the current situation of poor deployment of intelligent in China and tells the difficulties of subjective STR decision procedures. Findings Few Chinese FIs have deployed real intelligent STR systems, most are using rule-based filtering systems conformed to the objective STR supervisory regulations. To change the embarrassing situation, the regulators have tried to introduce self-regulatory mode which allows the FIs to define STR decision procedures themselves. Limited by the FIs’ ability of information sharing and investigation scope, FIs could hardly unveil the whole schema of a money laundering organization. The pursuant objective FIs can reach is to construct a system that could tell what the normal customer behaviors look like and extract all those falling out of the system’s expectations as suspicious activities. Research limitations/implications Only the core elements of the total intelligent STR system are discussed, that is, what, why and how about the customer behavior pattern recognition system. Besides this, a total solution should also use a watch list, reporting decision, cases management, risk control, etc. Originality/value This paper for the first time argues that the orientation of regulatory rules in China has actually hindered the spreading of really effective intelligent system for these years. The author creatively puts forward a solution to the difficult problem for FIs to spot criminal schema directly, instead the FIs should only be required to determine whether the transactions carrying out currently are falling within the expected behavior pattern scopes, which is under the FIs’ capabilities due to the internationally accepted obligations of “Know Your Customer”.

A financial ticket image intelligent recognition system based on deep learning

2021 ◽  
pp. 106955
Author(s):  
Hanning Zhang ◽  
Qinghua Zheng ◽  
Bo Dong ◽  
Boqin Feng
Keyword(s):  
Deep Learning ◽  
Recognition System ◽  
Intelligent Recognition

Zn diffusion and reflow behaviour of Sn-9Zn and Sn-8.5Zn-0.5Ag-0.01Al-0.1Ga solders on a Ni/Cu substrate under IR reflow

2014 ◽  
Vol 26 (2) ◽  
pp. 87-95 ◽  
Author(s):  
J. Mittal ◽  
K.L. Lin
Keyword(s):  
Theoretical Calculations ◽  
Research Work ◽  
Content Type ◽  
Diffusion Behavior ◽  
Zn Diffusion ◽  
Cu Substrate ◽  
Practical Applications ◽  
Substrate Interface ◽  
Solder Balls ◽  
Practical Implications

Purpose – This paper aims to compare the reflow and Zn diffusion behaviors in Sn-Zn and Sn-8.5Zn-0.5Ag-0.01Al-0.1Ga (5E) solders during soldering on a Ni/Cu substrate under infrared (IR) reflow. The study proposes a model on the effect of various elements particularly Zn diffusion behavior in the solders on the formation of intermetallic compounds (IMCs). Design/methodology/approach – The melting activities of two solders near their melting points on copper substrates are visualized in an IR reflow furnace. Reflowed solder joints were analyzed using scanning electron microscope and energy dispersive X-ray spectroscopy. Findings – Reflow behaviors of the solders are similar. During melting, solder balls are first merged into each other and then reflow on the substrate from top to bottom. Both solders show a reduced amount of Zn in the solder. Theoretical calculations demonstrate a higher Zn diffusion in the 5E solder; however, the amount of Zn actually observed at the solder/substrate interface is lower than Sn-9Zn solder due to the formation of ZnAg3 in the solder. A thinner IMC layer is formed at the interface in the 5E solder than the Sn-Zn solder. Research limitations/implications – The present work compares the 5E solder only with Sn-Zn solder. Additional research work may be required to compare 5E solder with other solders like Sn-Ag, SnAgCu, etc. to further establish its practical applications. Practical implications – The study ascertains the advantages of 5E solder over Sn-Zn solder for all practical applications. Originality/value – The significance of this paper is the understanding of the relation between reflow behavior of solders and reactivity of different elements in the solder alloys and substrate to form various IMCs and their influence on the formation of IMC layer at solder/substrate interface. Emphasis is provided for the diffusion behavior of Zn during reflow and respective reaction mechanisms.

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

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