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Journal articles on the topic 'Face comparison'

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1

Ondov, Brian, Nicole Jardine, Niklas Elmqvist, and Steven Franconeri. "Face to Face: Evaluating Visual Comparison." IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (2019): 861–71. http://dx.doi.org/10.1109/tvcg.2018.2864884.

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Nikolaievskyi, O. Yu, O. V. Skliarenko, and A. I. Sidorchuk. "ANALYSIS AND COMPARISON OF FACE DETECTION APIS." Telecommunication and information technologies, no. 4 (2019): 39–45. http://dx.doi.org/10.31673/2412-4338.2019.043945.

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3

Singh, Aruni. "Comparison of face Recognition Algorithms on Dummy Faces." International journal of Multimedia & Its Applications 4, no. 4 (2012): 121–35. http://dx.doi.org/10.5121/ijma.2012.4411.

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4

Cho, Miyoung, and Youngsook Jeong. "Face recognition performance comparison between fake faces and live faces." Soft Computing 21, no. 12 (2016): 3429–37. http://dx.doi.org/10.1007/s00500-015-2019-4.

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5

Megreya, Ahmed M. "Feature-by-feature comparison and holistic processing in unfamiliar face matching." PeerJ 6 (February 26, 2018): e4437. http://dx.doi.org/10.7717/peerj.4437.

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Identity comparisons of photographs of unfamiliar faces are prone to error but imperative for security settings, such as the verification of face identities at passport control. Therefore, finding techniques to improve face-matching accuracy is an important contemporary research topic. This study investigates whether matching accuracy can be enhanced by verbal instructions that address feature comparisons or holistic processing. Findings demonstrate that feature-by-feature comparison strategy had no effect on face matching. In contrast, verbal instructions focused on holistic processing made f
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G., Nirmala Priya. "Comparison of Partially Occluded Face Detection and Recognition Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 201–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202099.

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7

Cutillo, Denise, Vivian Weinblatt, Naomi Nakata, Amy Cronister, Fei Wang, and Alan E. Donnenfeld. "Comparison of Face to Face and Telephone Genetic Counseling." Obstetrics & Gynecology 101, Supplement (2003): 19S—20S. http://dx.doi.org/10.1097/00006250-200304001-00041.

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8

Dwyer, Dominic M., and Matei Vladeanu. "Perceptual learning in face processing: Comparison facilitates face recognition." Quarterly Journal of Experimental Psychology 62, no. 10 (2009): 2055–67. http://dx.doi.org/10.1080/17470210802661736.

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CUTILLO, D. "Comparison of face to face and telephone genetic counseling." Obstetrics & Gynecology 101, no. 4 (2003): S19—S20. http://dx.doi.org/10.1016/s0029-7844(02)02793-x.

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10

O'Malley, Claire, Steve Langton, Anne Anderson, Gwyneth Doherty-Sneddon, and Vicki Bruce. "Comparison of face-to-face and video-mediated interaction." Interacting with Computers 8, no. 2 (1996): 177–92. http://dx.doi.org/10.1016/0953-5438(96)01027-2.

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11

van Dam, Chris, Raymond Veldhuis, and Luuk Spreeuwers. "Face reconstruction from image sequences for forensic face comparison." IET Biometrics 5, no. 2 (2016): 140–46. http://dx.doi.org/10.1049/iet-bmt.2015.0036.

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12

Dnyanesh, Walwadkar. "Deep Learning Model for Face Identification using Localization of Faces." International Journal of Innovative Science and Research Technology 7, no. 3 (2022): 314–17. https://doi.org/10.5281/zenodo.6385751.

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An unknown face in a face dataset can be identified using a deep learning model and a wide range of issues can be addressed based on it. Identifying celebrities' faces for tagging in photos or other deep learning functions is one example. It can be used to identify missing people, identify criminals, verify government documents, collect images, and search for images. To address this issue. (1) The paper provides a comparison between the challenges and solutions proposed by the researchers and (2) the use of VGG Face models with localized faces to solve organizational face comparison proble
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13

White, David, P. Jonathon Phillips, Carina A. Hahn, Matthew Hill, and Alice J. O'Toole. "Perceptual expertise in forensic facial image comparison." Proceedings of the Royal Society B: Biological Sciences 282, no. 1814 (2015): 20151292. http://dx.doi.org/10.1098/rspb.2015.1292.

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Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experi
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14

Saha, Rajib, Debotosh Bhattacharjee, and Sayan Barman. "Comparison of Different Face Recognition Method Based On PCA." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 10, no. 4 (2014): 2016–22. http://dx.doi.org/10.24297/ijmit.v10i4.626.

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This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using:
 (a) Eigen faces and
 (b) applying Principal Component Analysis (PCA) on image.
 The aim is to successfully demonstrate the human face recognition using Principal component analysis & comparison of Manhattan distance, Eucleadian distance & Chebychev distance for face matching.
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15

Sá, Maria José, Carlos Miguel Ferreira, and Sandro Serpa. "Virtual and Face-To-Face Academic Conferences: Comparison and Potentials." Journal of Educational and Social Research 9, no. 2 (2019): 35–47. http://dx.doi.org/10.2478/jesr-2019-0011.

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Abstract Academic conferences have always been privileged spaces and moments for the dissemination of new scientific knowledge, as well as for social interaction and for the establishment and development of social networks among scientists. However, the virtual dimension of conferences, in which individuals are not physically present in the same place, begins to emerge as an increasingly used possibility, which implies a different framing of these scientific events. This paper seeks to comparatively analyse several models of academic conferences, putting forth their advantages, limitations and
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Julliard, Davina, John Vassiliadis, Justin Bowra, et al. "Comparison of supine and upright face-to-face cadaver intubation." American Journal of Emergency Medicine 56 (June 2022): 87–91. http://dx.doi.org/10.1016/j.ajem.2022.03.029.

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17

Delaney, G., S. Jacob, R. Iedema, M. Winters, and M. Barton. "Comparison of face-to-face and videoconferenced multidisciplinary clinical meetings." Australasian Radiology 48, no. 4 (2004): 487–92. http://dx.doi.org/10.1111/j.1440-1673.2004.01349.x.

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18

Hubackova, Sarka, and Ilona Semradova. "Comparison of on-line Teaching and Face-to-face Teaching." Procedia - Social and Behavioral Sciences 89 (October 2013): 445–49. http://dx.doi.org/10.1016/j.sbspro.2013.08.875.

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19

Rakover, Sam S., Rani Amit Bar-On, and Anna Gliklich. "Comparing Inverted Faces to Upright Faces Using Similarity or Mental Rotation." American Journal of Psychology 135, no. 2 (2022): 161–67. http://dx.doi.org/10.5406/19398298.135.2.03.

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Abstract A major interest of research in face recognition lies in explaining the face inversion effect, in which the recognition of an inverted face is less successful than that of an upright face. However, prior research has devoted little effort to examining how the cognitive system handles comparison between inverted and upright faces. The results of a preparatory experiment and two following experiments support the conclusion that the comparison is based more on visual similarity than on a mental rotation of the inverted face to an upright face. Visual similarity is based on certain elemen
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20

Borkar, Yasar, Reeve Mascarenhas, Shubham Tambadkar, and Jayanand P. Gawande. "Comparison of Real-Time Face Detection and Recognition Algorithms." ITM Web of Conferences 44 (2022): 03046. http://dx.doi.org/10.1051/itmconf/20224403046.

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With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so o
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21

Kukharev, G. A., K. Buda, and N. L. Shchegoleva. "Methods of face photo-sketch comparison." Pattern Recognition and Image Analysis 24, no. 1 (2014): 102–13. http://dx.doi.org/10.1134/s1054661814010076.

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22

Holubnycha, Liudmyla, Tetiana Shchokina, and Natalia Soroka. "Face-to-Face Teaching and Learning: The Problem of Quality." Educational Challenges 29, no. 1 (2024): 58–71. http://dx.doi.org/10.34142/2709-7986.2024.29.1.04.

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The article deals with the traditional format of teaching and learning from the point of view of its quality, in particular the advantages and disadvantages of face-to-face education in comparison with online training. The purpose is to check whether the classroom training process is of better quality than online teaching and learning on the basis of the real case of teaching English at Yaroslav Mudryi National Law University. The tasks are to find out the pluses of offline training in comparison with the online format; to reveal the minuses of the traditional educational form in comparison wi
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23

HAMADA, Yuri, Naoki TAKAHASHI, and Hiroko SHOJI. "A Comparison of Online and Face-to-Face Consensus-building Processes." Transactions of Japan Society of Kansei Engineering 21, no. 1 (2022): 41–48. http://dx.doi.org/10.5057/jjske.tjske-d-21-00027.

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24

Andres, Hayward P. "A comparison of face‐to‐face and virtual software development teams." Team Performance Management: An International Journal 8, no. 1/2 (2002): 39–48. http://dx.doi.org/10.1108/13527590210425077.

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25

Pritchard, Ruie Jane, and Donna Morrow. "Comparison of Online and Face-to-Face Peer Review of Writing." Computers and Composition 46 (December 2017): 87–103. http://dx.doi.org/10.1016/j.compcom.2017.09.006.

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26

Holmes, Courtney M., and Kelly A. Kozlowski. "A Preliminary Comparison of Online and Face-to-Face Process Groups." Journal of Technology in Human Services 33, no. 3 (2015): 241–62. http://dx.doi.org/10.1080/15228835.2015.1038376.

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27

Cheng, Colin C., Dennis Krumwiede, and Chwen Sheu. "Online Audio Group Discussions: A Comparison with Face-to-Face Methods." International Journal of Market Research 51, no. 2 (2009): 1–18. http://dx.doi.org/10.1177/147078530905100211.

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The performance of online focus groups has been extensively documented, but the extant research primarily emphasised the online typing method. In contrast, other potential methods have received little attention, especially online audio. Using a range of objective and subjective measures, this study compares the effectiveness of online audio and face-to-face (FTF) methods. The statistical results indicate that more information, better quality of answers, greater group interaction, greater satisfaction and more openness were generated in online audio than in FTF discussions. Meanwhile, the same
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28

Abdul Razak, Farihana, Afidah Osoman, and Azurawati Zaidi. "COMPARISON OF EXAMINATION RESULTS FOR ONLINE AND FACE-TO-FACE CLASSES." International Journal of Law, Government and Communication 8, no. 33 (2023): 61–69. http://dx.doi.org/10.35631/ijlgc.833005.

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When the Covid-19 virus spread all over the world, it has affected the education system at higher education institutions. In order to prevent the spread of the disease, a shift from face-to-face lectures to online lectures has taken place. Additionally, the present examination system approach has been changed online to benefit those in need. The objective of this article is to explore the examination results of Accounting Faculty students who took the course “Introduction to Partnership and Company Law”, which is code LAW346. The analysis of the examination results is based on two categories:
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29

Agbolade, Olalekan, Azree Shahrel Ahmad, and . "A Two Dimensional Facial Features Analysis for Gender-based Comparison Using Morphometrics Approach." International Journal of Engineering & Technology 7, no. 4.31 (2018): 214–19. http://dx.doi.org/10.14419/ijet.v7i4.31.23370.

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Making a gender comparison between male and female is not a difficult task for human beings but the science of gender comparison of faces by humans is completely unfathomable due to commonality of gender comparison in both humans and other animal species. Significant gender differences between masculine and feminine exist in many facial regions such as eyes, nose, mouth, cheek and chin; which have not been critically looked into. This research characterizes and analyzes the gender comparison in the human face as a function of face features and identifies the features which contribute significa
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Aashish, Kamath, and A. Vijayalakshmi. "Comparison of Viola-Jones And Kanade-Lucas-Tomasi Face Detection Algorithms." Oriental journal of computer science and technology 10, no. 1 (2017): 151–59. http://dx.doi.org/10.13005/ojcst/10.01.20.

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Face detection technologies are used in a large variety of applications like advertising, entertainment, video coding, digital cameras, CCTV surveillance and even in military use. It is especially crucial in face recognition systems. You can’t recognise faces that you can’t detect, right? But a single face detection algorithm won’t work in the same way in every situation. It all comes down to how the algorithm works. For example, the Kanade-Lucas-Tomasi algorithm makes use of spatial common intensity transformation to direct the deep search for the position that shows the best match. It is muc
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Alam, Mohammad Jahangir, Tanjia Chowdhury, and Md. Shahzahan Ali. "A smart login system using face detection and recognition by ORB algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 1078–87. https://doi.org/10.11591/ijeecs.v20.i2.pp1078-1087.

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We can identify human faces using a web Camera which is known as Face Detection. This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image. This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of th
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Elbert, Endah Setyaningsih, and Lamto Widodo. "Comparative Analysis of Haar Cascade Classifier, Dlib, and Mediapipe for Face Recognition." ELECTRON Jurnal Ilmiah Teknik Elektro 6, no. 1 (2025): 1–8. https://doi.org/10.33019/electron.v6i1.240.

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Technological developments are having considerable effects on a lot of industries, particularly in the security sector. One of the important technologies in security sector is face recognition. Face recognition is a technology that verify and identify individual identity using face. There are many processes that involved in face recognition technology such as face detection methods. Face detection is a process of searching for faces in images. Each face detection method has different way to searching the face in image. It can affect the performance of face recognition technology itself. In thi
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Gedik, Onur, and Ayşe Demirhan. "Comparison of the Effectiveness of Deep Learning Methods for Face Mask Detection." Traitement du Signal 38, no. 4 (2021): 947–53. http://dx.doi.org/10.18280/ts.380404.

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The usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face
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Rahmad, Cahya, Kohei Arai, Rosa Asmara, Ekojono Ekojono, and Dimas Putra. "Comparison of Geometric Features and Color Features for Face Recognition." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 541–51. http://dx.doi.org/10.22266/ijies2021.0228.50.

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Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color
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35

Żochowska, Anna, Maria M. Nowicka, Michał J. Wójcik, and Anna Nowicka. "Self-face and emotional faces—are they alike?" Social Cognitive and Affective Neuroscience 16, no. 6 (2021): 593–607. http://dx.doi.org/10.1093/scan/nsab020.

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Abstract The image of one’s own face is a particularly distinctive feature of the self. The self-face differs from other faces not only in respect of its familiarity but also in respect of its subjective emotional significance and saliency. The current study aimed at elucidating similarities/dissimilarities between processing of one’s own face and emotional faces: happy faces (based on the self-positive bias) and fearful faces (because of their high perceptual saliency, a feature shared with self-face). Electroencephalogram data were collected in the group of 30 participants who performed a si
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36

Blau, Gary, Sherry Jarrell, Anthony Seeton, Tim Young, Kelly Grace, and Michael Hughes. "Proposing an Expanded Measure for Comparing Online/Hybrid to Face-to-face Courses." Journal of Education and Development 2, no. 2 (2018): 1. http://dx.doi.org/10.20849/jed.v2i2.400.

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Online education continues to increase. With increased online offerings, it is important to evaluate the integrity or equivalence of online/hybrid courses relative to face-to-face (F2F) courses. This study used three separate samples of business undergraduates taking both online/hybrid and F2F courses in the same semester (i.e., mixed course delivery format), across summer, fall and spring semesters. Eight items were used to assess students’ perceived favorability of online courses (PFoOC) compared to F2F courses. Across all three samples, two related but distinct sources for course comparison
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37

Rieke-Zapp, D. H., and E. Trinkl. "Face to face - close range inspection of head vases." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5 (August 21, 2017): 601–4. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w5-601-2017.

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Several hundred attic head vases are known worldwide and stored in museums and collections. In 1929, Beazley has categorized twenty groups (A-W) based on stylistic properties and historic methodology. Head vases are assembled in several steps, most important for our comparison is the moulding of the head area. While the other parts of head vases like the size of the handle and the painting can differ significantly from each other, one can notice similarities in the head shapes of the same group. Since molds were used to shape the heads, our initial hypothesis was to perform a quantitative comp
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38

Asmaa, Yasin, Ahmed Bana, El Hadary Reem, and Hasan Jazyah Yahia. "INDEPENDENT INTEGRATED E-TRAINING ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 12 (2018): 146–52. https://doi.org/10.5281/zenodo.2532224.

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Several programming contests are being held every year searching for the best team in programming. To achieve the first place in the context, training is a crucial factor for participants; where meeting regularly and face to face is important issue. In some cases, meeting face to face is not available for competent, so this research develops an algorithm for training and examining the source code of user. The algorithm is based on designing an additional thread to monitor and control the process of testing the source code. The algorithm is efficient and achieves high percentage of success.
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Ma, Hua, Hui Liu, Yazhen Gong, Jianjun Jin, and Xianqiang Mao. "A comparison of mode effects between face-to-face and drop-off contingent valuation surveys." China Agricultural Economic Review 7, no. 3 (2015): 510–27. http://dx.doi.org/10.1108/caer-01-2014-0010.

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Purpose – The purpose of this paper is to examine the practice potential of self-administered drop-off as a survey mode for contingent valuation (CV) studies. Design/methodology/approach – This paper conducts an empirical comparison of mode effects of two survey methods for improved ecological services in Beijing. Data were collected from a CV survey, which has two subsamples, one using face-to-face interviews and the other employing self-administered drop-off surveys. Findings – There is some evidence of social desirability bias in the face-to-face interviews for the participation question; h
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40

Frederiksen, Erling. "Design and properties of face to face couplers for microphone comparison calibration." Journal of the Acoustical Society of America 103, no. 5 (1998): 2913. http://dx.doi.org/10.1121/1.422086.

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41

Underhill, Christina, and Murrey G. Olmsted. "An Experimental Comparison of Computer-Mediated and Face-to-Face Focus Groups." Social Science Computer Review 21, no. 4 (2003): 506–12. http://dx.doi.org/10.1177/0894439303256541.

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42

Gandell, Terry, and Ann Sutton. "Comparison of AAC interaction patterns in face-to-face and telecommunications conversations." Augmentative and Alternative Communication 14, no. 1 (1998): 3–10. http://dx.doi.org/10.1080/07434619812331278156.

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43

Tachakra, Sapal, Maria Lynch, Roger Newson, et al. "A comparison of telemedicine with face-to-face consultations for trauma management." Journal of Telemedicine and Telecare 6, no. 1_suppl (2000): 178–81. http://dx.doi.org/10.1258/1357633001934591.

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We compared the accuracy of teleconsultations for minor injuries with face-to-face consultations. Two hundred patients were studied. Colour change, swelling, decreased movement, tenderness, instability, radiological examination, severity of illness, treatment and diagnosis were recorded for both telemedicine and face-to-face consultations. Colour change showed an accuracy of 97%, presence of swelling or deformity of 98%, diminution of joint movement of 95%, presence of tenderness of 97%, weight bearing and gait of 99%, and radiological diagnosis of 98%. The severity of illness or injury was ov
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44

Tutty, Jeremy I., and James D. Klein. "Computer-mediated instruction: a comparison of online and face-to-face collaboration." Educational Technology Research and Development 56, no. 2 (2007): 101–24. http://dx.doi.org/10.1007/s11423-007-9050-9.

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Tutty, Jeremy I., and James D. Klein. "Computer-mediated instruction: a comparison of online and face-to-face collaboration." Educational Technology Research and Development 56, no. 4 (2008): 507. http://dx.doi.org/10.1007/s11423-008-9092-7.

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Alder, Rachel, Kanika Dua, and Rehana Iqbal. "Operator position for videolaryngoscopy: comparison of traditional and face-to-face positions." Trends in Anaesthesia and Critical Care 30 (February 2020): e116. http://dx.doi.org/10.1016/j.tacc.2019.12.284.

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47

Lee, Victoria S., Jeanne D. Johnston, Anne P. Massey, and Celeste DeVaneaux. "Comparison of a Face-to-Face verses Virtual World Weight Loss Program." Medicine & Science in Sports & Exercise 43, Suppl 1 (2011): 716. http://dx.doi.org/10.1249/01.mss.0000401987.11558.c6.

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Liu, Xiao, Yoshie Sawada, Takako Takizawa, et al. "Doctor-Patient Communication: A Comparison betweenTelemedicine Consultation and Face-to-Face Consultation." Internal Medicine 46, no. 5 (2007): 227–32. http://dx.doi.org/10.2169/internalmedicine.46.1813.

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49

Reeves, Philip M., and Rayne A. Sperling. "A comparison of technologically mediated and face-to-face help-seeking sources." British Journal of Educational Psychology 85, no. 4 (2015): 570–84. http://dx.doi.org/10.1111/bjep.12088.

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50

Hox, Joop J., and Edith D. De Leeuw. "A comparison of nonresponse in mail, telephone, and face-to-face surveys." Quality and Quantity 28, no. 4 (1994): 329–44. http://dx.doi.org/10.1007/bf01097014.

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