Fig 4 - uploaded by Luis Humberto Sanchez Medel
Content may be subject to copyright.
Download
View publication
The face detection algorithm uses the information of the skin detection to identify the circle sin the image, once the bigger circle is identified, the face is cropped to reduce the time to process the facial gestures Facial gestures detection using morphological filters. The facial gestures is information contained in the internal face contour as the Fig. 4 shows, once the face is cropped an image differential is used to remove all color and leaving only the edges of the image, the result  

The face detection algorithm uses the information of the skin detection to identify the circle sin the image, once the bigger circle is identified, the face is cropped to reduce the time to process the facial gestures Facial gestures detection using morphological filters. The facial gestures is information contained in the internal face contour as the Fig. 4 shows, once the face is cropped an image differential is used to remove all color and leaving only the edges of the image, the result  

Source publication
Table 1 Classification of the IWs direction using eyes and mouth.
Fig. 3. The result image after the sixth space is a gray image, where...
Table 3 Results of the facial gestures detection, these results are...
Fig. 4. The face detection algorithm uses the information of the skin...
+2 Fig. 5. The mouth is detected using pattern matching, once is found the...
Human Computer Interaction using Facial gestures to control a Wheelchair for tetraplegic users
Conference Paper
Full-text available
  • Oct 2008
Resumen: La Interacción Humano Computadora (HCI) es un campo en constante evolución, teclado, mouse y joystick son algunos de los dispositivos utilizados para interactuar con la computadora, sin embargo nuevas formas de interacción se están desarrollando como el control por audio y/o video. Los pacientes impedidos como los tetrapléjicos necesitan e...
Cite
Download full-text

Similar publications

Fig. 3 Comparison of single channel eigenvalues before and after...
Fig. 4 Principal component contribution rate
Fig. 5 Principal component analysis of training set and test set
Electrode sleeve
+6 Identification results after dimensionality reduction
Surface EMG hand gesture recognition system based on PCA and GRNN
Article
Full-text available
  • May 2020
The principal component analysis method and GRNN neural network are used to construct the gesture recognition system, so as to reduce the redundant information of EMG signals, reduce the signal dimension, improve the recognition efficiency and accuracy, and enhance the feasibility of real-time recognition. Using the means of extracting key informat...
View