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zl@t}^l@%0~loa4FWvDVtNdfnx8ic&OBJLIq0^gDWO8x&r%z-j8WFDSY?w&sGwhzdl RNH7?ICcAM%Nk^IN{{eb~uGatn diff --git a/paper/paper.tex b/paper/paper.tex index 753060d..1ef0257 100644 --- a/paper/paper.tex +++ b/paper/paper.tex @@ -391,12 +391,20 @@ \subsection{Accuracy} \subsection{Performance} -For our performance testing, we have conducted 5 tests. The are the same as the -accuracy tests except that the calibration phase is not needed. Our results are -computed by obtaining the amount of usable frames from a testing session and -dividing it by how long the session was. The sampling rate was 17.5 FPS ($\pm$ -4.5). This is a strong indication that our system is sufficient to run on a -vast range of different devices. +We have conducted 5 tests to measure the performance of $\Name$. The tests are +the same as the accuracy tests except that the calibration phase is not needed. +Our results are computed by obtaining the amount of usable frames from a +testing session and dividing it by how long the session was. The sampling rate +was 17.5 FPS ($\pm$ 4.5). This is a strong indication that our system is +sufficient to run on a vast range of different devices. + +Our performance suffers due different lighting conditions. Since the +implementation of the Haar detector in JavaScript currently does not support +tree-based cascades, different lighting conditions affect the amount of frames +that eyes were detected. We have found the best results when the user has +limited light projected directly on the face. The distance to the camera is +also an impeding factor on the performance. The user needs to be about 1 ft +from the webcam for the best results. \section{Discussion}