In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models. ; https://www.edusoft.ro/brain/index.php/brain/article/view/814/920
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)
2018-05-01
BRAIN. Broad Research in Artificial Intelligence and Neuroscience 9(2) 139-154
Miscellaneous
Electronic Resource
English
DDC: | 629 |
British Library Conference Proceedings | 1994
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