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Analysis of Microbiome DNA on Frequently Touched Items and from palm prints

Journal of Forensic Science and Medicine 2016年第2期2卷 74-77页

作者： Xue Yao Wenli Liu Junping Han Guangqian Pei Yigang Tong Yaping Luo School of Criminal Science and Technology People’s Public Security University of China1State Key Laboratory of Pathogen and BiosecurityBeijing Institute of Microbiology and Epidemiology.BeijingChina

Limited by the sensitivity of laboratory techniques,conventional human DNA analysis of touch DNA on frequently used items and prints does not always provide satisfactory *** this study,microbiome DNA on personal computers,cell phones,and palm prints was analyzed and *** sample collection,DNA extraction,polymerase chain reaction amplification,library preparation,and sequencing,data were analyzed using the QIIME 1.8.0 *** unifrac distance between the right palm skin and the right side of a keyboard,the right palm skin and the mouse,and the left side of the keyboard and the left palm skin was 0.258850,0.265474,and 0.214098,*** after palm prints were left for 1 week,microbial community structures were still quite similar to those of samples collected from the palm skin on the day they were left(weighted unifrac distance was 0.270885).

关键词： Cell phone keyboard metagenomics palm prints

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Novel Multimodal Biometric Feature Extraction for Precise Human Identification

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Intelligent Automation & Soft Computing 2023年第5期36卷 1349-1363页

作者： J.Vasavi M.S.Abirami Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia

In recent years,biometric sensors are applicable for identifying impor-tant individual information and accessing the control using various identiﬁers by including the characteristics like aﬁngerprint,palm print,iris recognition,and so ***,the precise identiﬁcation of human features is still physically chal-lenging in humans during their lifetime resulting in a variance in their appearance or *** response to these challenges,a novel Multimodal Biometric Feature Extraction(MBFE)model is proposed to extract the features from the noisy sen-sor data using a modiﬁed Ranking-based Deep Convolution Neural Network(RDCNN).The proposed MBFE model enables the feature extraction from differ-ent biometric images that includes iris,palm print,and lip,where the images are preprocessed initially for further *** extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the fea-ture extraction model and then testing the model with different sets of input *** simulation is performed in matlab to test the efﬁcacy of the modal over multi-modal datasets and the simulation result shows that the proposed meth-od achieves increased accuracy,precision,recall,and F1 score than the existing deep learning feature extraction *** performance improvement of the MBFE Algorithm technique in terms of accuracy,precision,recall,and F1 score is attained by 0.126%,0.152%,0.184%,and 0.38%with existing Back Propaga-tion Neural Network(BPNN),Human Identiﬁcation Using Wavelet Transform(HIUWT),Segmentation Methodology for Non-cooperative Recognition(SMNR),Daugman Iris Localization Algorithm(DILA)feature extraction techni-ques respectively.

关键词： Multimodalbiometric feature extraction ranking-baseddeepconvolution neural network noisy sensor data palm prints lip biometric iris recognition

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