Face Detection Technique Based on Skin Color and Facial Features
Paper
Alajel, K. M., Xiang, W. and Lies, John. 2010. "Face Detection Technique Based on Skin Color and Facial Features." 9th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA'10). England 20 - 22 Feb 2009 World Scientific and Engineering Academy and Society.
| Paper/Presentation Title | Face Detection Technique Based on Skin Color and Facial Features |
|---|---|
| Presentation Type | Paper |
| Authors | Alajel, K. M., Xiang, W. and Lies, John |
| Journal or Proceedings Title | Proceedings of the 9th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA'10) |
| Year | 2010 |
| Publisher | World Scientific and Engineering Academy and Society |
| ISBN | 9789604741571 |
| Conference/Event | 9th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA'10) |
| Event Details | 9th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA'10) Parent International Conference on Signal Processing, Robotics and Automation Delivery In person Event Date 20 to end of 22 Feb 2009 Event Location England Event Venue University of Cambridge |
| Abstract | Face detection is an essential first step in face recognition systems with the purpose of localizing and extracting the face region from the background. Apart from increasing the efficiency of face recognition systems, face detection technique also opens up the door of opportunity for application areas such as content based image retrieval, video encoding, video conferencing, crowd surveillance and intelligent human computer interfaces. In this paper, we propose a new face detection approach which is capable of detecting human faces from complex backgrounds. A skin color modeling process is proposed to the face segmentation process. Image enhancement is then used to improve the features of face candidates before feeding to the face object classifier which is based on the modified Hausdorff distance. The overall performance of the face detection system is evaluated with a successful rate of 87.5%. |
| Keywords | Face detection; skin color; facial features; face segmentation; modified Hausdroff distance; image enhancement |
| Public Notes | There are no files associated with this item. |
| Byline Affiliations | University of Southern Queensland |
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