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Journal articles on the topic 'Body recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Rasberry, C. N., T. M. Adams, P. A. Finnicum, and M. B. Dean. "BODY IMAGE RECOGNITION." Medicine & Science in Sports & Exercise 34, no. 5 (2002): S172. http://dx.doi.org/10.1097/00005768-200205001-00957.

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2

Knoblich, Günther. "Self-recognition: body and action." Trends in Cognitive Sciences 6, no. 11 (2002): 447–49. http://dx.doi.org/10.1016/s1364-6613(02)01995-2.

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3

Nakajima, Chikahito, Massimiliano Pontil, Bernd Heisele, and Tomaso Poggio. "Full-body person recognition system." Pattern Recognition 36, no. 9 (2003): 1997–2006. http://dx.doi.org/10.1016/s0031-3203(03)00061-x.

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4

Ahmed, Ferdous, A. S. M. Hossain Bari, and Marina L. Gavrilova. "Emotion Recognition From Body Movement." IEEE Access 8 (2020): 11761–81. http://dx.doi.org/10.1109/access.2019.2963113.

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5

Reed, Catherine L., Valerie E. Stone, Senia Bozova, and James Tanaka. "The Body-Inversion Effect." Psychological Science 14, no. 4 (2003): 302–8. http://dx.doi.org/10.1111/1467-9280.14431.

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Researchers argue that faces are recognized via the configuration of their parts. An important behavioral finding supporting this claim is the face-inversion effect, in which inversion impairs recognition of faces more than nonface objects. Until recently, faces were the only class of objects producing the inversion effect for untrained individuals. This study investigated whether the inversion effect extends to human body positions, a class of objects whose exemplars are structurally similar to each other. Three experiments compared the recognition of upright and inverted faces, houses, and b

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6

Zhang, Jian, and Wan Juan Song. "Body Recognition Based on Depth Image." Applied Mechanics and Materials 631-632 (September 2014): 414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.414.

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The text introduces the research status of depth image in the pattern recognition and the application in the body recognition. Aiming at the problem that the image recognition shot by common camera has declined performance under the factors of illumination, posture, shielding, and the like, the body parts are distinguished and judged by taking Kinect equipment promoted by Microsoft as the platform, analyzing the features of the depth picture obtained by the Kinect camera and putting forwards to the local gradient features of comprehensive point features and the gradient features; and the elbow

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7

Pilz, Karin S., and Ian M. Thornton. "Idiosyncratic body motion influences person recognition." Visual Cognition 25, no. 4-6 (2016): 539–49. http://dx.doi.org/10.1080/13506285.2016.1232327.

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8

Bharti, Jyoti, and M. K. Gupta. "Gait Recognition using Shoulder Body Joint." International Journal of Computer Applications 53, no. 4 (2012): 49–52. http://dx.doi.org/10.5120/8413-2053.

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9

ZHANG, Keye, Mingming ZHANG, Tiantian LIU, Wenbo LUO, and Weiqi HE. "The inversion effect of body recognition." Advances in Psychological Science 27, no. 1 (2019): 27. http://dx.doi.org/10.3724/sp.j.1042.2019.00027.

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10

WANG, SHOUJIA, WENHUI LI, BO FU, HONGYIN NI, and CONG WANG. "A ROBUST MOVING BODY RECOGNITION METHOD." Journal of Advanced Manufacturing Systems 11, no. 02 (2012): 107–14. http://dx.doi.org/10.1142/s0219686712500096.

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At present, moving body recognition is one of the most active areas of research in the field of computer vision and is used widely in all kinds of videos. But the recognition accuracy of these methods has changed negatively because of the complexity of the background. In this paper, we put forward a robust recognition method. First, we obtain the moving body by tripling the temporal difference method. And then we eliminate noise from these images by mathematical morphology. Finally, we use three-scanning notation method to mark and connect the connected domain. This new method is more accurate

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11

Tao, Weidong, and Hongjin Sun. "Configural processing in body posture recognition." NeuroReport 24, no. 16 (2013): 903–10. http://dx.doi.org/10.1097/wnr.0000000000000017.

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12

Miller, J. William. "Probabilistic models for ore body recognition." Mathematical Geology 21, no. 5 (1989): 559–78. http://dx.doi.org/10.1007/bf00894669.

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13

Yamamoto, Masanobu. "Image Engineering of Human Face and Body. Recognition Technologies. Gesture Recognition." Journal of the Institute of Image Information and Television Engineers 51, no. 8 (1997): 1128–31. http://dx.doi.org/10.3169/itej.51.1128.

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14

Fdez, Javier, and Michita Imai. "Nonverbal Communication Assistance System Based on Body Recognition in the Field of Education." International Journal of Machine Learning and Computing 9, no. 6 (2019): 885–61. http://dx.doi.org/10.18178/ijmlc.2019.9.6.883.

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15

Yang, Xiaodong, Meiling Fang, Aifeng Ren, et al. "Reverse recognition of body postures using on‐body radio channel characteristics." IET Microwaves, Antennas & Propagation 11, no. 9 (2017): 1212–17. http://dx.doi.org/10.1049/iet-map.2016.0990.

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16

Jefferies, Kiri, Keith R. Laws, and Naomi A. Fineberg. "Superior face recognition in Body Dysmorphic Disorder." Journal of Obsessive-Compulsive and Related Disorders 1, no. 3 (2012): 175–79. http://dx.doi.org/10.1016/j.jocrd.2012.03.002.

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17

Buhlmann, Ulrike, Richard J. McNally, Nancy L. Etcoff, Brunna Tuschen-Caffier, and Sabine Wilhelm. "Emotion recognition deficits in body dysmorphic disorder." Journal of Psychiatric Research 38, no. 2 (2004): 201–6. http://dx.doi.org/10.1016/s0022-3956(03)00107-9.

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18

Porter, Richard H., Rene D. Balogh, Jennifer M. Cernoch, and Christie Franchi. "Recognition of kin through characteristic body odors." Chemical Senses 11, no. 3 (1986): 389–95. http://dx.doi.org/10.1093/chemse/11.3.389.

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19

Simpson, Travis T., Susan L. Wiesner, and Bradford C. Bennett. "Dance Recognition System Using Lower Body Movement." Journal of Applied Biomechanics 30, no. 1 (2014): 147–53. http://dx.doi.org/10.1123/jab.2012-0248.

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The current means of locating specific movements in film necessitate hours of viewing, making the task of conducting research into movement characteristics and patterns tedious and difficult. This is particularly problematic for the research and analysis of complex movement systems such as sports and dance. While some systems have been developed to manually annotate film, to date no automated way of identifying complex, full body movement exists. With pattern recognition technology and knowledge of joint locations, automatically describing filmed movement using computer software is possible. T

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20

Moro, V., S. Pernigo, R. Avesani, et al. "Visual body recognition in a prosopagnosic patient." Neuropsychologia 50, no. 1 (2012): 104–17. http://dx.doi.org/10.1016/j.neuropsychologia.2011.11.004.

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21

de Gelder, Beatrice, and Nouchine Hadjikhani. "Non-conscious recognition of emotional body language." NeuroReport 17, no. 6 (2006): 583–86. http://dx.doi.org/10.1097/00001756-200604240-00006.

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22

h, o., and a. o. "RECOGNITION OF BODY PART USING NEURAL NETWORKS." Theoretical & Applied Science 74, no. 06 (2019): 233–39. http://dx.doi.org/10.15863/tas.2019.06.74.27.

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23

Calderara, Simone, Andrea Prati, and Rita Cucchiara. "Markerless body part tracking for action recognition." International Journal of Multimedia Intelligence and Security 1, no. 1 (2010): 76. http://dx.doi.org/10.1504/ijmis.2010.035972.

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24

Seifert, Ludovic, Vladislavs Dovgalecs, Jérémie Boulanger, Dominic Orth, Romain Hérault, and Keith Davids. "Full-body movement pattern recognition in climbing*." Sports Technology 7, no. 3-4 (2014): 166–73. http://dx.doi.org/10.1080/19346182.2014.968250.

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25

Wu, Qin, and Guodong Guo. "Gender Recognition from Unconstrained and Articulated Human Body." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/513240.

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Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-b

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26

Van den Stock, Jan, Isabelle Peretz, Julie Grèzes, and Beatrice de Gelder. "Instrumental Music Influences Recognition of Emotional Body Language." Brain Topography 21, no. 3-4 (2009): 216–20. http://dx.doi.org/10.1007/s10548-009-0099-0.

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27

Gessaroli, Erica, Veronica Andreini, Elena Pellegri, and Francesca Frassinetti. "Self-face and self-body recognition in autism." Research in Autism Spectrum Disorders 7, no. 6 (2013): 793–800. http://dx.doi.org/10.1016/j.rasd.2013.02.014.

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28

Piana, Stefano, Alessandra Staglianò, Francesca Odone, and Antonio Camurri. "Adaptive Body Gesture Representation for Automatic Emotion Recognition." ACM Transactions on Interactive Intelligent Systems 6, no. 1 (2016): 1–31. http://dx.doi.org/10.1145/2818740.

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29

Wang, Weiyi, Valentin Enescu, and Hichem Sahli. "Adaptive Real-Time Emotion Recognition from Body Movements." ACM Transactions on Interactive Intelligent Systems 5, no. 4 (2016): 1–21. http://dx.doi.org/10.1145/2738221.

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30

Kleinsmith, Andrea, and Nadia Bianchi-Berthouze. "Affective Body Expression Perception and Recognition: A Survey." IEEE Transactions on Affective Computing 4, no. 1 (2013): 15–33. http://dx.doi.org/10.1109/t-affc.2012.16.

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31

Kadambi, Akila, and Hongjing Lu. "Individual Differences in Self-recognition from Body Movements." Journal of Vision 18, no. 10 (2018): 1039. http://dx.doi.org/10.1167/18.10.1039.

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32

Tang, Jinshan, Xiaoming Liu, Huaining Cheng, and Kathleen M. Robinette. "Gender Recognition Using 3-D Human Body Shapes." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41, no. 6 (2011): 898–908. http://dx.doi.org/10.1109/tsmcc.2011.2104950.

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33

Yu, Naigong, and Jian Lv. "Human body posture recognition algorithm for still images." Journal of Engineering 2020, no. 13 (2020): 322–25. http://dx.doi.org/10.1049/joe.2019.1146.

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34

Pawar, T., S. Chaudhuri, and S. P. Duttagupta. "Body Movement Activity Recognition for Ambulatory Cardiac Monitoring." IEEE Transactions on Biomedical Engineering 54, no. 5 (2007): 874–82. http://dx.doi.org/10.1109/tbme.2006.889186.

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35

Park, Jae Wan, Seok Jin Lee, and Chil Woo Lee. "Upper-Body Pose Recognition Using Cylinder Pattern Model." Applied Mechanics and Materials 571-572 (June 2014): 781–87. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.781.

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In this paper, we propose cylindrical coordinate system which can analyze upper-body pose of the depth images correctly. This method extracts the part of human body from the depth images, and we configure the center of the part as origin of the cylindrical coordinate system. And we configure multi-layered cylinders based on the origin, then, we can analyze pose using the pattern which is crossed depth images namely cylinders and upper-body. Since the crossed point of the cylinders and upper-body is obtained as brightness values, it can extract to convert feature vector of the cylindrical coord

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36

Wang, Zhelong, Ming Guo, and Cong Zhao. "Badminton Stroke Recognition Based on Body Sensor Networks." IEEE Transactions on Human-Machine Systems 46, no. 5 (2016): 769–75. http://dx.doi.org/10.1109/thms.2016.2571265.

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37

Senecal, Simon, Louis Cuel, Andreas Aristidou, and Nadia Magnenat-Thalmann. "Continuous body emotion recognition system during theater performances." Computer Animation and Virtual Worlds 27, no. 3-4 (2016): 311–20. http://dx.doi.org/10.1002/cav.1714.

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38

Gonzalez-Sanchez, T., and D. Puig. "Real-time body gesture recognition using depth camera." Electronics Letters 47, no. 12 (2011): 697. http://dx.doi.org/10.1049/el.2011.0967.

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39

Hanssen, Eirik Nataas, Kristian Hovde Liland, Peter Gill, and Lars Snipen. "Optimizing body fluid recognition from microbial taxonomic profiles." Forensic Science International: Genetics 37 (November 2018): 13–20. http://dx.doi.org/10.1016/j.fsigen.2018.07.012.

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40

Wang, Ching-Wei, and Andrew Hunter. "Robust Pose Recognition of the Obscured Human Body." International Journal of Computer Vision 90, no. 3 (2010): 313–30. http://dx.doi.org/10.1007/s11263-010-0365-3.

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41

Ross, Paddy, and Tessa Flack. "Removing Hand Form Information Specifically Impairs Emotion Recognition for Fearful and Angry Body Stimuli." Perception 49, no. 1 (2019): 98–112. http://dx.doi.org/10.1177/0301006619893229.

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Emotion perception research has largely been dominated by work on facial expressions, but emotion is also strongly conveyed from the body. Research exploring emotion recognition from the body tends to refer to “the body” as a whole entity. However, the body is made up of different components (hands, arms, trunk, etc.), all of which could be differentially contributing to emotion recognition. We know that the hands can help to convey actions and, in particular, are important for social communication through gestures, but we currently do not know to what extent the hands influence emotion recogn

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42

Cha, Su Joung. "Comparison of Actual Body Shape and Recognition Body Shape of Old Aged Males." Journal of Basic Design & Art 20, no. 3 (2019): 391–405. http://dx.doi.org/10.47294/ksbda.20.3.30.

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43

Yu, Yang. "Accurate Recognition Method of Human Body Movement Blurred Image Gait Features Using Graph Neural Network." Mobile Information Systems 2021 (July 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/1684726.

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In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement

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44

Hood, Jacob. "Making the Body Electric: The Politics of Body-Worn Cameras and Facial Recognition in the United States." Surveillance & Society 18, no. 2 (2020): 157–69. http://dx.doi.org/10.24908/ss.v18i2.13285.

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This paper explores the rapid deployment of police body-worn cameras (BWCs) and the subsequent push for the integration of biometric technologies (i.e., facial recognition) into these devices. To understand the political dangers of these technologies, I outline the concept of “making the body electric” to provide a critical language for cultural practices of identifying, augmenting, and fixing the body through technological means. Further, I argue how these practices reinforce normative understandings of the body and its political functionality, specifically with BWCs and facial recognition. I

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45

Sapiński, Tomasz, Dorota Kamińska, Adam Pelikant, and Gholamreza Anbarjafari. "Emotion Recognition from Skeletal Movements." Entropy 21, no. 7 (2019): 646. http://dx.doi.org/10.3390/e21070646.

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Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We ana

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46

Koo, Ja, Se Cho, Na Baek, Min Kim, and Kang Park. "CNN-Based Multimodal Human Recognition in Surveillance Environments." Sensors 18, no. 9 (2018): 3040. http://dx.doi.org/10.3390/s18093040.

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In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research being performed on indoor human recognition. Previous research on indoor recognition has mainly focused on face recognition because the camera is usually closer to a person in an indoor environment than an outdoor environment. However, due to the nature of indoor surveillance cameras, which are installed near the ceiling and capture images from above i

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47

Chang, Zheng, Xiaojuan Ban, Qing Shen, and Jing Guo. "Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/528190.

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Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements) and the extreme learning machine (constructed as the classification artificial neural network of body movements), the paper applies the method to the machine vision of the body movement trajectory. In detail, the paper gives a detailed introduction abo

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48

Xiaojiu Li, Hao Liu, Yuxiu Wang, and Xiaozhi Li. "Body Type Recognition by Multi-class Support Vector Machines." International Journal of Advancements in Computing Technology 4, no. 20 (2012): 487–96. http://dx.doi.org/10.4156/ijact.vol4.issue20.57.

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49

Karovska Ristovska, A. "Recognition of Body Parts in Persons with Intellectual Disability." Годишен зборник на Филозофскиот факултет/The Annual of the Faculty of Philosophy in Skopje 70 (2017): 387–406. http://dx.doi.org/10.37510/godzbo1770387kr.

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50

Hock, Alyson, Hannah White, Rachel Jubran, and Ramesh S. Bhatt. "The whole picture: Holistic body posture recognition in infancy." Psychonomic Bulletin & Review 23, no. 2 (2015): 426–31. http://dx.doi.org/10.3758/s13423-015-0902-8.

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