Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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.

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

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To 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 features.In 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 characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.

HiddenTag:Enabling Person Identification Without Privacy Exposure

Person identification is the key to enable personalized services in smart homes,including the smart voice assistant,augmented reality,and targeted advertisement.Although 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 limitations.In 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 smartphones.Based upon the multi-path effects and human body absorption,we design suitable sound signals and acoustic features for constructing the human body signatures.In addition,we use CSI to trigger the system of acoustic sensing.Extensive experiments indicate that HiddenTag can distinguish multi-person in 10–15 s with 95.1%accuracy.We implement a prototype of HiddenTag with an online system by Android smartphones and maintain 84%–90%online accuracy.

Efficient Authentication System Using Wavelet Embeddings of Otoacoustic Emission Signals

Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security concerns.In our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric modality.TEOAE are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be impersonated.In this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature maps.The corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the subjects.Among the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier works.This performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.

Gait Recognition by Cross Wavelet Transform and Graph Model

In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter(QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 % average recognition rate.

Forensic Analysis of Sexual Dimorphism from Facial Photographs

Background:Techniques for identifying facial features and proving the individuality of a person’s facial features are used in forensic anthropology.Aims and Objectives:Acommon method in this area is cranial anthropometry,which has uses in facial reconstruction,locating unidentified remains,and identifying criminal suspects.Materials and Methods:A study analyzed 200 standard full-face photographs of 50 males and 50 females to compare frontal and lateral facial images and identify male and female facial traits.We located landmarks and measured the distances between them.The data were analyzed using statistical tests like t-tests and analysis of variance(ANOVA)tests.Result:The results showed that in 100%of the suggested measurements for both males and females,there were no statistically significant differences(P>0.05)between frontal and lateral views.The ANOVA test,however,showed that all suggested measurements showed differences in the lateral aspect,while only 76.92%of them showed differences between the male and female groups in the frontal aspect.Conclusion:In conclusion,statistically significant differences were found,rejecting the null hypothesis of equal means,when the level of significance(α)was lower than the P value(0.05).On the other hand,the null hypothesis was accepted when statistically insignificant differences were found and the level of significance was higher than the P value.Although there were differences between males and females in different views,the distances between landmarks in frontal and lateral views of the same person were generally similar.Measurements taken in the lateral aspect were more accurate and displayed greater sexual dimorphism variation compared to those taken in the frontal aspect.

Key Technology Research for Mobile Police Terminal Fingerprint Collection for Quick Comparison Using Automated Fingerprint Identification System

When the face of the inspected person and the photograph on their identification(ID)card cannot be clearly matched,the individual is undocumented,or the ID is forged,it is often difficult for the on‑site police to respond in time.This study proposes a number of key technologies for collecting fingerprints at mobile terminals for fast comparison using an automated fingerprint ID system(AFIS).These technologies ensure intelligent mobile fingerprint collection and allow the transmission of fingerprint information from the terminal to AFIS,over a wireless public security network for real‑time fingerprint comparison.This study also analyzes the feasibility and effectiveness of the proposed technologies for system design and the applicability of fingerprint ID algorithms.The system achieved good results in a test by the Shanghai Public Security Bureau Criminal Investigation Corps.

The Evaluation of Insertion and Deletion Polymorphism in Population and Personal Identification Amidst Chinese Populations

For comprehensive understanding of practical application and evaluation on the power of30 commonly used InDeis(Qiagen Investigator DIPplex®kit),we captured population data from 25 Chinese populations and employed F-statistics for population genetics analysis.The results indicated that the distributions of allelic frequencies among populations were in different levels.Furthermore,the phylogeny confoiming pairwise FST distances showed that the difierentiation of majority populations were consistent with their geographic locations and historic dispersals.We conduct the comprehensive correlation analysis between FST and heterozygosity of30 InDel loci and provided strong evidence for ongoing InDei loci selection.The Fst values of 30 InDels were calculated within 25 Chinese populations,and then,these loci were characterized definitely based on their roles in population genetics or individual identification.Data indicated that 17 InDels with FST<0.01 could be utilized regarding Chinese individual identification(total discrimination power=0.999985 and cumulative matching probability=0.00000009).We comprehensively reconstructed the population structure and filled the gap of evaluating the ability of InDels in personal as well as population identification.The application of InDel loci in the forensic area would convincingly promote the development matter of forensic population identification and personal discrimination.

Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception

Identity-recognition technologies require assistive equipment,whereas they are poor in recognition accuracy and expensive.To overcome this deficiency,this paper proposes several gait feature identification algorithms.First,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 proposed.Experimental 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.