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6 results for “Micro Expression Recognition”
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 2. Result of the implementation of EVM method.
<p>In our research, we focus on the parameter values in the second case. Then we convert the obtained video to the images sequence. The number of images is decreased after the implementation of the EVM method. The reason of this problem is the integration of multiple pixels in the images. The resulting output is a generic extract of all motions created in the face.</p> <p>In this study, two experiments are performed as follows: Phase1: investigating the emotional/unemotional detection on face in micro expressions. Phase2: investigating facial micro expressions recognition </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 4. The chart of average percent of emotional/unemotional detection in SMIC
<p>In Figure 4 the blue color curve represents the average percent of emotional/unemotional detection using the EVM method. The red color curve also represents the average percent of emotional/unemotional detection without this method. Table 1 shows the running time and speed in this experiment. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 1. Overall structure of micro expression recognition
<p>In this study we investigate the emotional/unemotional detection on the face and micro expression recognition. As expressed in Murthy & Jadon (2007) and Frank & Nöth (2003) the eigenface method is not able to perform micro expression recognition. So, we use the EVM method for micro expression recognition based on the eigenface method. Using this magnification method, the subtle motions are retrieved from the face during micro expression and those are not visible. In order to do this work, we need a dataset including the spontaneous subtle facial motions. Therefore, in this study we use two databases, SMIC and CASME. According to the obvious features in the EVM method for retrieving and displaying subtle motions, first we test these databases using this method. Then we evaluate the rate of emotional/unemotional detection in the face and micro expression recognition using the eigenface method. An overall structure of our research is shown in Figure 1. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
Performance analysis of micro-expression recognition over different sample image sizes.
<p>Performance of micro-expression recognition accuracy analyzed using different sample sizes with motion and geometric features. The sample sizes analyzed are: 140x170, 280x340, 560x680 and 1120x1360 using SMIC, CASMEII, CAS(ME)^2 and SAMM. The experiments were conducted using three different feature extraction setups: (i) optimized BiWOOF, (ii) full-face graph, and (iii) full-face graph with amplitude-based emotion magnification method (A-EMM). The results were compared with the results of the original BiWOOF presented in [1] as the baseline study. These files are published under CC0 license.</p> <p> </p> <p>References</p> <p>[1] Liong, S. T., See, J., Wong, K., & Phan, R. C. W. (2018). Less is more: Micro-expression recognition from video using apex frame. <em>Signal Processing: Image Communication</em>, <em>62</em>, 82-92.</p>
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International Brain Laboratory public data
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OpenNeuro
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