Relevant bibliographies by topics / Human identification

Academic literature on the topic 'Human identification'

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Published: 4 June 2021
Last updated: 4 May 2024

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Journal articles on the topic "Human identification"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Human identification"

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Kim, Brian A. (Brian Andrew) 1979. "Multi-source human identification." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29662.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 59-60).
In this thesis, a multi-source system for human identification is developed. The system uses three sources: face classifier, height classifier, and color classifier. In the process of developing this system, classifier combination and the integration of classifer outputs over sequences of data points were studied in detail. The method of classifier combination used relies on weighing classifiers based on the Maximum Likelihood estimation of class probabilities. The integration of classifer outputs, which is termed "temporal integration" in this thesis, has been developed to take advantage of the information implicitly contained in data correlated through time. In all experiments performed, temporal integration has improved classification, up to 40% in some cases. Meanwhile, the method of temporally integrating the outputs of multiple classifiers fused using our classifier weighting method outperforms all individual classifiers in the system.
by Brian A. Kim.
M.Eng.
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Johansson, Jonathan, and Daniel Wikdahl. "Human identification with radar." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34619.

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Hong, Jie. "Human gait identification and analysis." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7115.

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Human gait identification has become an active area of research due to increased security requirements. Human gait identification is a potential new tool for identifying individuals beyond traditional methods. The emergence of motion capture techniques provided a chance of high accuracy in identification because completely recorded gait information can be recorded compared with security cameras. The aim of this research was to build a practical method of gait identification and investigate the individual characteristics of gait. For this purpose, a gait identification approach was proposed, identification results were compared by different methods, and several studies about the individual characteristics of gait were performed. This research included the following: (1) a novel, effective set of gait features were proposed; (2) gait signatures were extracted by three different methods: statistical method, principal component analysis, and Fourier expansion method; (3) gait identification results were compared by these different methods; (4) two indicators were proposed to evaluate gait features for identification; (5) novel and clear definitions of gait phases and gait cycle were proposed; (6) gait features were investigated by gait phases; (7) principal component analysis and the fixing root method were used to elucidate which features were used to represent gait and why; (8) gait similarity was investigated; (9) gait attractiveness was investigated. This research proposed an efficient framework for identifying individuals from gait via a novel feature set based on 3D motion capture data. A novel evaluating method of gait signatures for identification was proposed. Three different gait signature extraction methods were applied and compared. The average identification rate was over 93%, with the best result close to 100%. This research also proposed a novel dividing method of gait phases, and the different appearances of gait features in eight gait phases were investigated. This research identified the similarities and asymmetric appearances between left body movement and right body movement in gait based on the proposed gait phase dividing method. This research also initiated an analysing method for gait features extraction by the fixing root method. A prediction model of gait attractiveness was built with reasonable accuracy by principal component analysis and linear regression of natural logarithm of parameters. A systematic relationship was observed between the motions of individual markers and the attractiveness ratings. The lower legs and feet were extracted as features of attractiveness by the fixing root method. As an extension of gait research, human seated motion was also investigated.
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Mohd, Hadi Pritam Helmi. "Facial creases in human identification." Thesis, University of Dundee, 2012. https://discovery.dundee.ac.uk/en/studentTheses/8d7c0427-13b1-40eb-870f-d01c9d795b7b.

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This thesis is divided into four Phases. The aim of each phase is to identify facial creases useful in human identification.In Phase 1, creases were analysed on peri- and post-embalmed cadavers in CAHID to establish whether or not there is any change to crease with facial bloating. Embalming was chosen to simulate effects seen on a bloated face during decomposition. The results suggested that creases are quite resilient and changes were only detected relating to creases located on the periphery of the face, particularly at areas where the skin is thick, such as at the cheek region. Two new creases not classified in literature were identified on the face; these creases were called vertical superciliary arch lines and the lateral nose crease. Manifestations of these creases were also seen on faces in Phase 2 and 3 of the research. Phase 2 focused on the application of facial creases for the identification of living individuals. Volunteers were obtained from the University of Aberdeen and University of Dundee. Phase 2 was divided further into Phase 2a and Phase 2b. Phase 2a focused on matching creases from video and photograph sources while Phase 2b focused on matching creases from 3D surface scans to face photographs. A higher match rate was obtained for Phase 2a, where the shadows of the creases on two different sources were similar, as compared to the 3D to 2D analysis in Phase 2b. A Bayesian conclusion scale was utilised to categorise the conclusion.Research in Phase 3 focused on establishing facial crease correlation with skull morphology. Material for the research was obtained from William Bass skeletal collection at the University of Tennessee which provided ante-mortem face photographs with related 3D skull surface scans. Superimposition of the creases on the face photographs with the skulls was conducted to enable the visual analysis of the crease location. The qualitative analysis indicated that the infraorbital crease follows the outline of the orbits in 52% of the total subjects. No correlation was obtained between the nasolabial fold (NLF) and the bony surface inferior to the location of the crease. However, the depth of the selected skeletal region indicated the NLF was detected in 95% of the subjects. Quantitative analysis was carried out with the aid of geometric morphometrics (GMM) to analyse the maxilla morphology to establish whether the morphology indicated crease morphology. Geometric morphometric analysis indicated that people with a strong NLF had a long and narrow maxillary region.The conclusions obtained in Phase 3 were tested through a blind study in Phase 4. Analyses of the NLF and infraorbital crease were conducted on the Helmer skull collection available in CAHID. Ten skulls were provided to the researcher for analysis without related ante-mortem photographs. Once analyses were completed, the ante-mortem photographs were supplied and conclusions were obtained by comparing the crease reconstruction to the face photograph. Correct reconstruction was obtained in six of the ten specimens (60%). One case was inconclusive due to poor photograph quality though the location of the crease region appeared to be correct. The three inaccurate results showed an overestimation of the NLF strength, though the location of the crease manifestation was correct.
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Reid, Daniel. "Human identification using soft biometrics." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/352293/.

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Humans naturally use descriptions to verbally convey the appearance of an individual. Eyewitness descriptions are an important resource for many criminal investigations. However, they cannot be used to automatically search databases featuring video or biometric data - reducing the utility of human descriptions in the search for the suspect. Soft biometrics are a new form of biometric identification which uses physical or behavioural traits that can be naturally described by humans. This thesis will explore how soft biometrics can be used alongside traditional biometrics, allowing video footage and biometric data to be searched using a description. To permit soft biometric identification the human description must be accurate, yet conventional descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe the differences between subjects. A database of facial and bodily comparative labels is introduced and analysed. Prior to use as a biometric feature, comparative descriptions must be anchored. Several techniques to convert multiple comparative labels into a single relative measurement are explored. Recognition experiments were conducted to assess the discriminative capabilities of relative measurements as a biometric. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using several machine learning techniques to determine relative measurements from gait biometric signatures. Retrieval results are presented showing the ability to automatically search video footage using comparative descriptions.
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Saleh, Mohamed Ibrahim. "Using Ears for Human Identification." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33158.

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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate.
Master of Science
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Sekiguchi, Kazumasa. "Analysis of human mitochondrial DNA heteroplasmy for human identification." Kyoto University, 2004. http://hdl.handle.net/2433/148349.

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Qi, Lin. "Autonomous Identification of Human Activity Regions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212052.

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Human activity regions (HARs) are human-centric semantic partitions where observing and/or interacting with humans is likely in indoor environments. HARs are useful for achieving successful human-robot interaction, such as in safe navigation around a building or to know where to be able to assist humans in their activities. In this thesis, a system is designed for generating HARs automatically based on data recorded by robots. This approach to generating HARs is to cluster the areas that are commonly associated with frequent human presence. In order to detect human positions, we employ state-of-the-art perception techniques. The environment that the robot patrols is assumed to be an indoor environment such as an office. We show how we can generate HARs in correct regions by clustering human position data. The experimental evaluations show that we can do so in different indoor environments, with data acquired from different sensors and that the system can handle noise.
Mänskliga aktivitetsregioner, HARs (Human Activity Regions) är människocentreraderegioner som ger en semantisk partitionering av inomhusmiljöer. HARs är användbara för att uppnå väl fungerande människarobot- interaktioner. I denna avhandling utformas ett system för att generera HARs automatiskt baserat på data från robotar. Detta görs genom att klustra observationer av människor för att på så vis få fram de områden som är associerade med frekvent mänsklig närvaro. Experiment visar att systemet kan hantera data som registrerats av olika sensorer i olika inomhusmiljöer och att det är robust. Framförallt genererar systemet en pålitlig partitionering av miljön.
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Ivanoiu, Alexandru Paul <1997&gt. "H.I.P.T - Human Identification and Protection Technology." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/22021.

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The project H.I.P.T it is a project build to protect and keep save all the people on the planet and at the same time reduce pollution. The project idea is based on creating a software or better a web application that allows us to interact with a chip that will get inserted under the skin. The chip will take the place of all the credit cards, id cards, passports, fidelity card or any type of card , document that there is in existence. The chip will be read using an NFC reader, from this NFC reader the unique code will be used by the software in order to identify each person on planet Earth and allowing the people to have full control over their data in any second of their lives.
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Fu, Tseung-yan Clara, and 符祥欣. "Identification of novel parvoviruses in human and animals." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43085301.

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Books on the topic "Human identification"

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Wu, Ziyan. Human Re-Identification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7.

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Timothy, Thompson, and Black Sue M, eds. Forensic human identification. Boca Raton: Taylor & Francis, 2007.

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Lion, Thomas, ed. Human Fungal Pathogen Identification. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6515-1.

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Bhanu, Bir, and Venu Govindaraju, eds. Multibiometrics for Human Identification. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511921056.

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Nixon, Mark S., Tieniu Tan, and Rama Chellappa. Human Identification Based on Gait. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-29488-9.

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Personal identification from human remains. Springfield, Ill., U.S.A: Charles C. Thomas, 1987.

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Trivedi, Deven N., Nimit D. Shah, Ashish M. Kothari, and Rohit M. Thanki. Dental Image Processing for Human Identification. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99471-0.

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Andrews, Dee H. Human factors issues in combat identification. Farnham, Surrey, England: Ashgate, 2009.

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Herz, Robert P. Human factors issues in combat identification. Farnham, Surrey, England: Ashgate, 2010.

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P, Herz Robert, and Wolf Mark B, eds. Human factors issues in combat identification. Farnham, Surrey, England: Ashgate, 2009.

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Book chapters on the topic "Human identification"

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Knott, Stephen. "Human identification." In Forensic Odontology, 167–84. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118864418.ch5.

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Wu, Ziyan. "The Problem of Human Re-Identification." In Human Re-Identification, 3–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_1.

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Wu, Ziyan. "Conclusions and Future Work." In Human Re-Identification, 99–101. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_10.

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Wu, Ziyan. "Features and Signatures." In Human Re-Identification, 13–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_2.

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Wu, Ziyan. "Multi-object Tracking." In Human Re-Identification, 23–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_3.

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Wu, Ziyan. "Surveillance Camera and Its Calibration." In Human Re-Identification, 29–39. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_4.

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Wu, Ziyan. "Calibrating a Surveillance Camera Network." In Human Re-Identification, 41–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_5.

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Wu, Ziyan. "Learning Viewpoint Invariant Signatures." In Human Re-Identification, 57–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_6.

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Wu, Ziyan. "Learning Subject-Discriminative Features." In Human Re-Identification, 77–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_7.

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Wu, Ziyan. "Dimension Reduction with Random Projections." In Human Re-Identification, 85–92. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40991-7_8.

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Conference papers on the topic "Human identification"

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Gkalelis, Nikolaos, Anastasios Tefas, and Ioannis Pitas. "Human identification from human movements." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413916.

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Ntuen, Celestine A., Evi H. Park, A. Johnson, and Jung H. Kim. "Experimental study of human interpretation of computer-generated image data." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172540.

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Burns, Brian, and Biswanath Samanta. "Human Identification for Human-Robot Interactions." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38496.

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In co-robotics applications, the robots must identify human partners and recognize their status in dynamic interactions for enhanced acceptance and effectiveness as socially interactive agents. Using the data from depth cameras, people can be identified from a person’s skeletal information. This paper presents the implementation of a human identification algorithm using a depth camera (Carmine from PrimeSense), an open-source middleware (NITE from OpenNI) with the Java-based Processing language and an Arduino microcontroller. This implementation and communication sets a framework for future applications of human-robot interactions. Based on the movements of the individual in the depth sensor’s field of view, the program can be set to track a human skeleton or the closest pixel in the image. Joint locations in the tracked human can be isolated for specific usage by the program. Joints include the head, torso, shoulders, elbows, hands, knees and feet. Logic and calibration techniques were used to create systems such as a facial tracking pan and tilt servomotor mechanism. The control system presented here sets groundwork for future implementation into student built animatronic figures and mobile robot platforms such as Turtlebot.
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Chawalitsittikul, Pongsatorn, and Nikom Suvonvorn. "Profile-based Human Action Recognition using Depth Information." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.770-039.

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Prestrude, Al M., and J. W. Ternes. "Optimizing substance detection by integration of canine-human team with machine technology." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172542.

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Kumar, Ajay, and Yingbo Zhou. "Human identification using KnuckleCodes." In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS). IEEE, 2009. http://dx.doi.org/10.1109/btas.2009.5339021.

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Hung, Tzu-Yi, Jiwen Lu, Junlin Hu, Yap-Peng Tan, and Yongxin Ge. "Activity-based human identification." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638077.

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Bruce, V. "Identification of human faces." In 7th International Conference on Image Processing and its Applications. IEE, 1999. http://dx.doi.org/10.1049/cp:19990396.

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Ma, Shangchang, Tadanao Zanma, and Muneaki Ishida. "Human Identification Based on Human Skill Models." In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.351.

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Nangtin, P., P. Kumhom, and K. Chamnongthai. "Human identification by using human part similarity." In 2007 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, 2007. http://dx.doi.org/10.1109/ispacs.2007.4445875.

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Reports on the topic "Human identification"

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Little, L. M. Automated Identification of Human Incursion. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/1558329.

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Wickes, Brian L. Molecular Identification of Human Fungal Pathogens. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada485729.

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Wickes, Brian L. Molecular Identification of Human Fungal Pathogens. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada503794.

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Nadimi, Sohail, Edward Hong, and Bir Bhanu. Multimodal Human Identification for Computer Security. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada430881.

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Wickes, Brian L. Molecular Identification of Human Fungal Pathogens. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada553261.

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Wickes, Brian L. Molecular Identification of Human Fungal Pathogens. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada470615.

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Smith, Gary J. Identification of Markers of Human Vascular Dynamics Exposed in the Human Vasculature of Human Prostate Xenografts by Androgen Deprivation. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada435042.

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Metaxas, D. Human Identification and Recognition of Emotional State from Visual Input. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada448621.

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Apidianakis, Georgios. Identification of Human Intestinal Bacteria that Promote or Inhibit Inflammation. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada607490.

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10

Chu, F. Analytical Strategies to Investigate the Hair Proteome for Human Identification. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1623332.

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