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Journal articles on the topic 'Human identification'

To see the other types of publications on this topic, follow the link: Human identification.

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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1

Zhang, Tongda, Xiao Sun, Yueting Chai, and Hamid Aghajan. "Human Computer Interaction Activity Based User Identification." International Journal of Machine Learning and Computing 4, no. 4 (2014): 354–58. http://dx.doi.org/10.7763/ijmlc.2014.v4.436.

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2

Montague, Mary-Louise, Michael S. W. Lee, and S. S. M. Hussain. "Human Error Identification." Archives of Otolaryngology–Head & Neck Surgery 130, no. 10 (October 1, 2004): 1153. http://dx.doi.org/10.1001/archotol.130.10.1153.

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3

Solla, Horacio E. "Human Rights and Identification of Human Skeletal Remains by Digital Skull-Photographic Comparisons in Uruguay." International Journal of Forensic Sciences 8, no. 2 (2023): 1–8. http://dx.doi.org/10.23880/ijfsc-16000307.

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Scientists make unique contributions in human rights cases by applying scientific and forensic techniques to criminal investigations. In human rights cases, evidence is often based solely on the oral testimonies of victims or witnesses. There is little doubt concerning the importance of oral testimonies. However, spoken evidence is much more effective when it is corroborated by physical evidence. In Latin America countries, experts such as forensic anthropologists, pathologists, and archaeologists contribute to human rights cases by aiding in death investigations and in the identification of victims remains. Forensic anthropologists are often called on for cases in which trauma analysis and identification of human skeletal remains is necessary. After 1984, Argentina, Chile and Guatemala have developed national forensic anthropology teams but they are not part of their judicial system as they are not governmental organizations. But, since 1992 in Uruguay, there is an official Forensic Anthropology Laboratory integrated to the judicial system directed by the author. Since 1992 at the present, this Laboratory has made more than 1700 official forensic anthropology report of cases and identified more than 200 people with ages ranging from 5 to 90 years old at time of death using traditional forensic anthropology techniques.

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4

Eom, Yong-Bin. "Microbial Forensics: Human Identification." Biomedical Science Letters 24, no. 4 (December 31, 2018): 292–304. http://dx.doi.org/10.15616/bsl.2018.24.4.292.

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5

Camara, Carmen, Pedro Peris-Lopez, Masoumeh Safkhani, and Nasour Bagheri. "ECGsound for human identification." Biomedical Signal Processing and Control 72 (February 2022): 103335. http://dx.doi.org/10.1016/j.bspc.2021.103335.

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6

Li, Bibo, Stephanie Oestreich, and Titia de Lange. "Identification of Human Rap1." Cell 101, no. 5 (May 2000): 471–83. http://dx.doi.org/10.1016/s0092-8674(00)80858-2.

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7

Tseng, Kuo-Kun, Lean Fu, Linlin Liu, Dachao Lee, Chao Wang, Lingao Li, and Yao Meng. "Human identification with electrocardiogram." Enterprise Information Systems 12, no. 7 (March 20, 2018): 798–819. http://dx.doi.org/10.1080/17517575.2018.1450526.

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8

Bost, X., G. Senay, M. El-Bèze, and R. De Mori. "Multiple topic identification in human/human conversations." Computer Speech & Language 34, no. 1 (November 2015): 18–42. http://dx.doi.org/10.1016/j.csl.2015.03.006.

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9

Boldrin, Luisa, and Jennifer E. Morgan. "Human satellite cells: identification on human muscle fibres." PLoS Currents 3 (January 19, 2012): RRN1294. http://dx.doi.org/10.1371/currents.rrn1294.

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10

Pyrc, Krzysztof, Ben Berkhout, and Lia van der Hoek. "Identification of new human coronaviruses." Expert Review of Anti-infective Therapy 5, no. 2 (April 2007): 245–53. http://dx.doi.org/10.1586/14787210.5.2.245.

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11

Cavalcanti Caputo, Isamara Geandra, Lais Ubaldo Antonio, Ana Paula do Rego Andre, Mariana Goncalves de Castro, Leticia Batista Pinto, Raphaela Duarte da Cunha, and Monica Santiago Oliveira Amaral Carvalho. "Cheiloscopy in the human identification." Foresic Research & Criminology International Journal 6, no. 5 (November 16, 2018): 371–74. http://dx.doi.org/10.15406/frcij.2018.06.00231.

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12

Budyakova, T. P. "Disinformation effect in human identification." Psychology and Law 8, no. 4 (2018): 99–114. http://dx.doi.org/10.17759/psylaw.2018080410.

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The article presents the results of an experimental study of psychological errors which occur when a person performing identification has been told false identification features before the actual procedure. We have revealed the mechanisms of disinformation effect: the generalization of working memory representations and involuntary mental information adding. Previous studies of the phenomenon of misinformation focused on the study of unintentional memorization, in the process of which the mechanism of an external implanted facility (misinformation) was introduced. In the experiments of other scientists, it was not possible to preset memorizing the elements of the event or the appearance of the subjects. It is shown that a preliminary instruction to memorize the features of an object does not reduce the disinformation effect. At the same time, a prior memorizing instruction can block the influence of gender, age and professional attitudes, but it does not block the subjective attitudes of a particular person. It was shown that reproducing facts some time later is not the main cause of identification errors.

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13

Sutar, Ankita, Shreya Bhagat, Shruti Shinde, Apeksha Kambale, and Prof Puja Patil. "Human Image Classification and Identification." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1034–36. http://dx.doi.org/10.22214/ijraset.2022.42397.

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Abstract: Face Recognition is in recent times used as a chunk of various applications. It is used as a piece of the makes use of images processing applications. The Raspberry Pi 4 is embedded system. It is low cost, a single-board computer used to lessen the complexity of systems in actual time applications. This project is especially based on python. Raspberry pi carries Camera slot Interface (CSI) to interface the raspberry pi camera. Here, Images captured by using the Raspberry Pi camera module are improved so that it will identify the specific region of image. This is used in the real time application of ‘Smart Attendance System’. We have proposed to implement a “Smart attendance system using Face Recognition”. This system makes use of Face recognition package which saves a number of time than conventional method. This system requires Raspberry Pi 4, Open CV, Python etc. The system uses Face Recognition Package. This system compares the image of the test and the training image and decide who is present and absent. If student is absent a message will be sent to their parent’s phone number. Student will be greeted with the voice when he/she come in front of camera by using text to speech mechanism. The system wishes “Happy Birthday” if student’s birthday is there by checking the birth date and current date. Keywords: Open CV, Face Recognition, Raspberry Pi, Face recognition package.

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14

Barros, Franciéllen de, Clemente Maia da Silva Fernandes, Barbara Kuhnen, José Scarso Filho, Marcelo Gonçalves, and Mônica da Costa Serra. "Paranasal sinuses and human identification." Research, Society and Development 10, no. 9 (July 31, 2021): e48710918161. http://dx.doi.org/10.33448/rsd-v10i9.18161.

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The characteristics of the paranasal sinuses (maxillary, frontal, sphenoid and ethmoid sinuses) are information of great relevance to Forensic Sciences, as their images can be used for human identification purposes. Due to their particularities, the paranasal sinuses provide valuable information for human identification, reducing the risk of errors during investigation by experts. Such structures are visualized from several imaging exams. This study evaluates the possibilities of human identification through the analysis of the paranasal sinuses, as well as the effectiveness of their analysis in estimating sex, age and ancestry. A comprehensive search was performed in the PubMed, SciELO, LILACS, and Web of Science databases. As inclusion criteria, texts that addressed the subject were selected. Imaging analysis of the frontal, maxillary and sphenoid sinuses is a useful tool for human identification, as well as for estimating sex, age and ancestry; usually provides a high level of accuracy. Regarding the ethmoid sinus, research is indicated to verify its use in human identification, as no publications on this specific subject were found. Additional research must be carried out (especially three-dimensional analysis of the paranasal sinuses), to develop standardized protocols, improving the work of experts, helping justice and society.

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15

Lalwani*, Natasha, Aishna Sharma, and Dr Mani Roja M. Edinburgh. "Biometric Identification using Human Ear." International Journal of Engineering and Advanced Technology 9, no. 1 (October 30, 2019): 4893–98. http://dx.doi.org/10.35940/ijeat.a2027.109119.

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Biometrics refers to the metrics of the human characteristics which has gained much popularity in recent times. It is a form of identification and access control. Widely used forms of biometrics are facial recognition, finger print recognition, iris recognition, etc. but the drawback is that most of these features change over time. The human ear is a cogent source of data to classify biometrically since its attributes do not change substantially as time progresses. This paper explores the field of ear biometric wherein the database images are re-sized to 128 x 256 pixels and then converted to grayscale image. Various transforms viz. Discrete Cosine Transform, Discrete Fourier Transform, Discrete Wavelet Transform are then applied to extract the features. The coefficients of the test image are compared with the coefficients of the registered database image. On comparison, Euclidean distance classifier is used to recognize the test image from the database. The database used consists of 25 subjects with 6 images per person out of which the initial 4 images are used to train the model, and the remaining 2 for testing. The outputs of various transforms were compared and the best accuracy obtained is 86% using Discrete Wavelet Transform.

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16

., Amruta P. Dharmadhikari. "HUMAN IDENTIFICATION USING FINGER IMAGES." International Journal of Research in Engineering and Technology 03, no. 05 (May 25, 2014): 840–43. http://dx.doi.org/10.15623/ijret.2014.0305154.

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17

M.Hashem, Kadhim, and Fatima Ghali. "Human Identification Using Foot Features." International Journal of Engineering and Manufacturing 6, no. 4 (July 8, 2016): 22–31. http://dx.doi.org/10.5815/ijem.2016.04.03.

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18

Johansson, R., M. Magnusson, and M. Akesson. "Identification of human postural dynamics." IEEE Transactions on Biomedical Engineering 35, no. 10 (1988): 858–69. http://dx.doi.org/10.1109/10.7293.

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19

UESAKA, Yuki, Ayumi TOMIYAMA, Kazuki YUHARA, Ryou KONDO, and Fumiya KITAYAMA. "Identification of Human Standing Behaviors." Proceedings of Ibaraki District Conference 2019.27 (2019): 507. http://dx.doi.org/10.1299/jsmeibaraki.2019.27.507.

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20

Lynnerup, Niels. "Forensic anthropology and human identification." Scandinavian Journal of Forensic Science 19, no. 1 (May 1, 2013): 16–38. http://dx.doi.org/10.2478/sjfs-2013-0005.

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21

Abebrese, Emmanuel L., Syed H. Ali, Zachary R. Arnold, Victoria M. Andrews, Katharine Armstrong, Lindsay Burns, Hannah R. Crowder, et al. "Identification of human short introns." PLOS ONE 12, no. 5 (May 17, 2017): e0175393. http://dx.doi.org/10.1371/journal.pone.0175393.

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22

Kim, Insook, Xiao-yan Chu, Seonyoung Kim, Chester J. Provoda, Kyung-Dall Lee, and Gordon L. Amidon. "Identification of a Human Valacyclovirase." Journal of Biological Chemistry 278, no. 28 (May 5, 2003): 25348–56. http://dx.doi.org/10.1074/jbc.m302055200.

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23

Perez Alea, Mileidys, Vincent Thomas, Guillaume Martin, and Saïd El Alaoui. "Identification of human salivary transglutaminases." Amino Acids 44, no. 1 (November 12, 2011): 245–50. http://dx.doi.org/10.1007/s00726-011-1142-5.

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24

Corradini, M. L., S. Fioretti, T. Leo, R. Piperno, M. Franceschini, and S. Stecchi. "Identification of Human Postural Control." IFAC Proceedings Volumes 23, no. 8 (August 1990): 255–61. http://dx.doi.org/10.1016/s1474-6670(17)52017-0.

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25

Wikstrom, A., G. J. J. van Doornum, W. G. V. Quint, J. T. Schiller, and J. Dillner. "Identification of human papillomavirus seroconversions." Journal of General Virology 76, no. 3 (March 1, 1995): 529–39. http://dx.doi.org/10.1099/0022-1317-76-3-529.

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26

Francesquini Junior, Luiz, DianaMaria Souza e. Couto, NíviaCristina Duran Gallassi, Stefany deLima Gomes, Viviane Ulbricht, JoãoSarmento Pereira Neto, and Eduardo Daruge Junior. "Brazilian's dental anthropometry: Human identification." Journal of Forensic Dental Sciences 11, no. 2 (2019): 73. http://dx.doi.org/10.4103/jfo.jfds_65_19.

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27

Sasakawa, Dai, Naoki Honma, Takeshi Nakayama, and Shoichi Iizuka. "Human Identification Using MIMO Array." IEEE Sensors Journal 18, no. 8 (April 15, 2018): 3183–89. http://dx.doi.org/10.1109/jsen.2018.2803157.

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28

Kumar, A., and Yingbo Zhou. "Human Identification Using Finger Images." IEEE Transactions on Image Processing 21, no. 4 (April 2012): 2228–44. http://dx.doi.org/10.1109/tip.2011.2171697.

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29

Gausas, Roberta E., Russell S. Gonnering, Bradley N. Lemke, Richard K. Dortzbach, and Deborah D. Sherman. "Identification of Human Orbital Lymphatics." Ophthalmic Plastic & Reconstructive Surgery 15, no. 4 (July 1999): 252–59. http://dx.doi.org/10.1097/00002341-199907000-00006.

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30

Clarke, Roger. "Human Identification in Information Systems." Information Technology & People 7, no. 4 (December 1994): 6–37. http://dx.doi.org/10.1108/09593849410076799.

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31

Gittleman, B., T. E. Dwan, and C. S. Smiley. "System identification: human tracking response." IEEE Transactions on Education 35, no. 1 (1992): 31–37. http://dx.doi.org/10.1109/13.123414.

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32

Van Brunt, Jennifer. "Human Disease Genes Elude Identification." Nature Biotechnology 7, no. 2 (February 1989): 120. http://dx.doi.org/10.1038/nbt0289-120.

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33

Ramasamy, K., M. Jayasheela, E. Gomathi, and E. Udayakumar. "Human Biometric Authentication using Dental Features." Indian Journal of Forensic Medicine and Pathology 14, no. 3 (September 15, 2021): 339–46. http://dx.doi.org/10.21088/ijfmp.0974.3383.14321.3.

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Now a days, Human identification has become a major issue. Many biometric authentications like fingerprint, Iris, hand veins, etc., were introduced to identify individuals and they have more complex features in it. Under numerous occasions like disasters, traditional biometric attributes like fingerprints and so on, may not work in light of their contrariness in such cases. They may lead once in a while towards high blunder rate and furthermore the investigation is more perplexing. Biometric using dental features are becoming a leading technique for human identification. Research says that dental features of persons are unique by nature. By research, it was found that the maxillary and mandible jaws of every individual are found to be unique. Here, the majority of the likenesses are coordinated with the goal that person can be actually recognized by its novel element. Along these lines dental biometrics gets need over rather in considering highlights and examination perspective. In this sense, it has been a savvy way to deal with investigate the new fields of exploration. In this paper, a human dental biometric image processing system has been implemented using MATLAB software for easy identification and this system is rotation invariant with greater accuracy.

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34

Caputo, I. G. C., F. B. Prado, E. Daruge Junior, and V. F. Muglia. "Frontal Sinuses in Human Identification: Literature Review." Brazilian Journal of Forensic Sciences, Medical Law and Bioethics 1, no. 1 (2011): 8–14. http://dx.doi.org/10.17063/bjfs1(1)y20112.

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35

Darwish, Saad M., Adel A. El-Zoghabi, and Oday A. Hassen. "A Modified Walk Recognition System for Human Identification Based on Uncertainty Eigen Gait." International Journal of Machine Learning and Computing 4, no. 4 (2014): 346–53. http://dx.doi.org/10.7763/ijmlc.2014.v4.435.

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36

Starykovych, M., S. Souchelnytskyi, O. Fayura, O. Abrahamovych, M. Abrahamovych, N. Lukavetskyy, R. Stoika, and Y. Kit. "Identification of cortactin molecular forms in human urine and their possible diagnostic value." Ukrainian Biochemical Journal 93, no. 4 (September 13, 2021): 103–10. http://dx.doi.org/10.15407/ubj93.04.103.

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37

Wei, Xin, Kai Yu, Hao Wu, Chen Shen, Huiyu Li, Ruina Liu, Qinru Sun, and Zhenyuan Wang. "Species identification of teeth of human and non-human." Forensic Science International 333 (April 2022): 111205. http://dx.doi.org/10.1016/j.forsciint.2022.111205.

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38

Wang, Qi, Wei Li, Ruina Liu, Kai Zhang, Haohui Zhang, Shuanliang Fan, and Zhenyuan Wang. "Human and non-human bone identification using FTIR spectroscopy." International Journal of Legal Medicine 133, no. 1 (March 16, 2018): 269–76. http://dx.doi.org/10.1007/s00414-018-1822-8.

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39

Novick, Gabriel E., Corina C. Novick, Juan Yunis, Emilio Yunis, Kianfa Martinez, George G. Duncan, Gary M. Troup, et al. "Polymorphic human specificAlu insertions as markers for human identification." Electrophoresis 16, no. 1 (1995): 1596–601. http://dx.doi.org/10.1002/elps.11501601263.

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40

Tan, Peng Hui, Keng Poh Wee, and Peter Sahelangi. "Remembering the Musi – SilkAir Flight MI 185 Crash Victim Identification." Annals of the Academy of Medicine, Singapore 36, no. 10 (October 15, 2007): 861–66. http://dx.doi.org/10.47102/annals-acadmedsg.v36n10p861.

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On 19 December 1997, SilkAir Flight MI 185, a Boeing B737-300 airliner crashed into the Musi River near Palembang, Southern Sumatra, enroute from Jakarta, Indonesia to Singapore. All 104 passengers and crew onboard were killed. Of the human remains recovered, 6 positive identifications were made, including that of one Singaporean. Two of the identifications were by dental records, 2 by fingerprints, 1 by age estimation and 1 by personal effects. This paper describes the crash victim identification of Flight MI 185. The authors were part of an Indonesia-Singapore forensic team deployed for 3 weeks in Palembang to assist the Indonesian authorities in human remains identification. Key words: Boeing 737, Dental radiology, Disaster victim identification, Forensic odontology

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41

P., Prabha, Jitendra Sheetlani, and Rajmohan Pardeshi. "Fingerprint based Automatic Human Gender Identification." International Journal of Computer Applications 170, no. 7 (July 17, 2017): 1–4. http://dx.doi.org/10.5120/ijca2017914910.

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42

Saranya, M., G. L. Infant Cyril, and A. Umamageswari. "Ear Features based Human Identification System." Asian Journal of Research in Social Sciences and Humanities 6, no. 7 (2016): 820. http://dx.doi.org/10.5958/2249-7315.2016.00466.4.

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43

Khalifa,Wahhab, Alanssari, M. A. O Ahmed, Hany S. ,. H. I. ,. A. N. ,. M. A. O. Ahmed. "Fingerprint Segmentation Approach for Human Identification." Applied Mathematics & Information Sciences 13, no. 4 (July 1, 2019): 515–21. http://dx.doi.org/10.18576/amis/130401.

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44

Kumar, V. K. Narendira, and B. Srinivasan. "Ear Biometrics in Human Identification System." International Journal of Information Technology and Computer Science 4, no. 2 (March 9, 2012): 41–47. http://dx.doi.org/10.5815/ijitcs.2012.02.06.

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45

Kakabadse, Nada K., Alexander Kouzmin, and Andrew P. Kakabadse. "Radio-Frequency Identification and Human Tagging." International Journal of E-Politics 1, no. 2 (April 2010): 29–45. http://dx.doi.org/10.4018/jep.2010040103.

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Technological innovation continually shifts boundaries of possibilities and at the same time challenges ethical dimensions. Radio Frequency Identification (RFID) technology challenges both what is possible and what is ethical. On this basis, this paper incorporates an analysis of RFID development and provides penetrating insight concerning the ethical issues society faces and will face. Attention is given to the burgeoning and challenging field of human-centred RFID technology and its impact on the individual’s privacy, safety, civil liberty and on society in general. The paper briefly outlines the history of RFID and identifies three broad themes concerning the application of RFID tags for the purpose of human “branding”. In particular, the social rights impact of implants in humans, the potential and actual damage of implants to the physical health of the person, and the ownership, use and miss-use of information collected through implant technology, act as the central themes. In conclusion, the authors identify the critical areas surrounding human branding that require research and public deliberation.

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46

Radhi, Muna Abdul Hussain. "Human Identification Model Considering Biometrics Features." Journal La Multiapp 3, no. 4 (August 26, 2022): 198–206. http://dx.doi.org/10.37899/journallamultiapp.v3i4.692.

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In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.

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47

Pacaux-Lemoine, M. P., and F. Vanderhaegen. "BCD MODEL FOR HUMAN STATE IDENTIFICATION." IFAC Proceedings Volumes 40, no. 16 (2007): 378–83. http://dx.doi.org/10.3182/20070904-3-kr-2922.00066.

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48

Sabir, Azhin Tahir, Mohammed H. Ahmed, Abdulbasit K. Faeq, and Halgurd S. Maghdid. "Human gait identification using Kinect sensor." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 142–46. http://dx.doi.org/10.24017/science.2017.3.37.

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This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods.

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49

Matsumoto, Yasuhiro, Takemi Enomoto, and Takashi Masuko. "Identification of Truncated Human Glutamate Transporter." Tohoku Journal of Experimental Medicine 187, no. 2 (1999): 173–82. http://dx.doi.org/10.1620/tjem.187.173.

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50

McCarty, Daniel J. "Crystal Identification in Human Synovial Fluids." Rheumatic Disease Clinics of North America 14, no. 2 (August 1988): 253–67. http://dx.doi.org/10.1016/s0889-857x(21)00963-7.

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