Pub Date : 1900-01-01DOI: 10.55011/staiqc.2022.2104
K. K. Jena, K. Prasad. K, Rajermani Thinakaran
The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.
在当前形势下，口罩的重要性是整个人类社会关注的一个重大问题。所有人都应该佩戴FM，以降低因几种疾病而感染的机会。在不同人群密集的场所，对未佩戴FM的人群进行跟踪，提醒其佩戴FM，以降低不同疾病的传播。因此，将人脸图像(hfi)分类为带面具人脸图像(HFWMIs)和无面具人脸图像(HFWOMIs)类型是这种情况下的基本要求。在这项工作中，提出了基于机器智能(MI)的hfi分类方法，将hfi分为hfwmi和hfwomi类型。该方法主要采用逻辑回归(LRG)、支持向量机(SVMN)、随机森林(RFS)和神经网络(NNT)方法的叠加(杂交)来进行分类。将该方法与LRG、SVMN、RFS、NNT、Decision Tree (DTR)、AdaBoost (ADB)、Naïve贝叶斯(NBY)、k -最近邻(KNNH)和随机梯度下降(SGDC)等其他基于机器学习(ML)的方法进行性能分析。所提出的方法和其他基于ML的方法已经使用基于Python的Orange 3.26.0实现。在这项工作中，从Kaggle源中提取了200个hffwmi和200个hfwomi。所有方法的性能评估使用性能参数，如分类精度(CA)， F1，精度(PR)和召回(RC)。结果表明，与LRG、SVMN、RFS、NNT、DTR、ADB、NBY、KNNHand SGD等基于ML的分类方法相比，本文方法在CA、F1、PR和RC方面具有更好的分类效果。
{"title":"A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask","authors":"K. K. Jena, K. Prasad. K, Rajermani Thinakaran","doi":"10.55011/staiqc.2022.2104","DOIUrl":"https://doi.org/10.55011/staiqc.2022.2104","url":null,"abstract":"The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.","PeriodicalId":231409,"journal":{"name":"Sparklinglight Transactions on Artificial Intelligence and Quantum Computing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125193901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.55011/staiqc.2022.2102
Bhagya Jyothi K, Vasudeva
Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.
{"title":"Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams","authors":"Bhagya Jyothi K, Vasudeva","doi":"10.55011/staiqc.2022.2102","DOIUrl":"https://doi.org/10.55011/staiqc.2022.2102","url":null,"abstract":"Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.","PeriodicalId":231409,"journal":{"name":"Sparklinglight Transactions on Artificial Intelligence and Quantum Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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