scholarly journals Calculating screen to face distance

Author(s):  
Ivan Ludvig Tereshko

A method of calculating screen to face distance is presented. The method relies on average distance between the user’s eyes (pupillary distance) and does not require calibration. The algorithm is implemented as an Android application using face detection technologies provided by Android.<br><br>

2020 ◽  
Author(s):  
Ivan Ludvig Tereshko

A method of calculating screen to face distance is presented. The method relies on average distance between the user’s eyes (pupillary distance) and does not require calibration. The algorithm is implemented as an Android application using face detection technologies provided by Android.<br><br>


2020 ◽  
Author(s):  
Ivan Ludvig Tereshko

A method of calculating screen to face distance is presented. The method relies on average distance between the user’s eyes (pupillary distance) and does not require calibration. The algorithm is implemented as an Android application using face detection technologies provided by Android.<br><br>


2017 ◽  
Vol 10 (1) ◽  
pp. 151-159
Author(s):  
Kamath Aashish ◽  
A. Vijayalakshmi

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 much faster than other traditional techniques for checking far fewer potential matches between pictures. Similarly, another common face detection algorithm is the Viola-Jones algorithm that is the most widely used face detection algorithm. It is used in most digital cameras and mobile phones to detect faces. It uses cascades to detect edges like the nose, the ears etc. However, if there is a group of people and their faces are close to each other, the algorithm might not work that well as edges tend to overlap in a crowd. It might not detect individual faces. Therefore, in this work, we test both the Viola-Jones and the Kanade-Lucas-Tomasi algorithm for each image to find out which algorithm works best in which scenario.


1979 ◽  
Vol 44 ◽  
pp. 209-213
Author(s):  
B. Rompolt

The aim of this contribution is to turn attention to a peculiarity of location of the filaments (quiescent prominences) with respect to the boundaries of the coronal holes. It is generally known that quiescent prominences are located at some distance from the boundary of coronal holes. My intention was to check whether the average distance between the nearest border of a coronal hole and the prominence is comparable to the average horizontal extension of a helmet structure overlying the prominence. As well as, whether this average distance depends upon the orientation of the long axis of the prominence with respect to the nearest boundary of the coronal hole.


CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xuejin Wan ◽  
Shangfo Huang ◽  
Bowen Du ◽  
Rui Sun ◽  
Jiong Wang ◽  
...  

2011 ◽  
Vol 4 (7) ◽  
pp. 188-190 ◽  
Author(s):  
Kallakunta. Ravi Kumar ◽  
◽  
Shaik Akbar

2012 ◽  
Vol 2 (3) ◽  
pp. 140-142
Author(s):  
Aabid A.S Mulani ◽  
◽  
Sagar A Patil ◽  
Yogesh R Khedkar

2010 ◽  
Vol 130 (11) ◽  
pp. 2031-2038
Author(s):  
Kohki Abiko ◽  
Hironobu Fukai ◽  
Yasue Mitsukura ◽  
Minoru Fukumi ◽  
Masahiro Tanaka
Keyword(s):  

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