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Dynamic Hand Gesture-Based person identification Using Leap Motion and Machine Learning Approaches

Computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： person identification leap motion hand gesture random forest support vector machine

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HiddenTag:Enabling person identification Without Privacy Exposure

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ZTE Communications 2021年第3期19卷 3-12页

作者： QIU Chen DAI Tao GUO Bin YU Zhiwen LIU Sicong Northwestern Polytechnical University Xi’an 710072China Chang’an University Xi’an 710064China

person identification is the key to enable personalized services in smart homes,including the smart voice assistant,augmented reality,and targeted *** research in the past decades in person identification has brought technologies with high accuracy,existing solutions either require explicit user interaction or rely on images and video processing,and thus suffer from cost and privacy *** this paper,we introduce a devicefree personal identification system–HiddenTag,which utilizes smartphones to identify different users via profiling indoor activities with inaudible sound and channel state information(CSI).HiddenTag sends inaudible sound and senses its diffraction and multi-path reflection using *** upon the multi-path effects and human body absorption,we design suitable sound signals and acoustic features for constructing the human body *** addition,we use CSI to trigger the system of acoustic *** experiments indicate that HiddenTag can distinguish multi-person in 10–15 s with 95.1%*** implement a prototype of HiddenTag with an online system by Android smartphones and maintain 84%–90%online accuracy.

关键词： person identification acoustic sensing CSI smart home

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An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1689-1710页

作者： Md.Abdur Rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia Department of Computer Science and Engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh Department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh Department of Electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh Department of Electrical and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one *** are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through ***,such systems are susceptible to forgery,posing security *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and *** key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive ***-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite *** meticulous amalgamation resulted in a robust set of 91 *** enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent *** the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting ***,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual ***,our experimental results unde

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception

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Big Data Mining and Analytics 2021年第4期4卷 223-232页

作者： Changjie Wang Zhihua Li Benjamin Sarpong Department of Computer Science and Technology School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi 214122China

Identity-recognition technologies require assistive equipment,whereas they are poor in recognition accuracy and *** overcome this deficiency,this paper proposes several gait feature identification ***,in combination with the collected gait information of individuals from triaxial accelerometers on smartphones,the collected information is preprocessed,and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset;then,with the multimodal characteristics of the collected biological gait information,a Convolutional Neural Network based Gait Recognition(CNN-GR)model and the related scheme for the multimodal features are developed;at last,regarding the proposed CNN-GR model and scheme,a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are *** results show that the proposed algorithms perform well in recognition accuracy,the confusion matrix,and the kappa statistic,and they have better recognition scores and robustness than the compared algorithms;thus,the proposed algorithm has prominent promise in practice.

关键词： gait recognition person identification deep learning multimodal feature fusion

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