Abstract
For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YCbCr color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.
| Original language | English |
|---|---|
| Pages (from-to) | 78-82 |
| Number of pages | 5 |
| Journal | Journal of Electronic Science and Technology |
| Volume | 2015 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
Funding
The National Basic Research Program of China (973 Program) under Grant No.2012CB215202 and the National Natural Science Foundation of China under Grant No.51205046
Keywords
- Adaboost
- Cost-sensitive learning
- Face detection
- Skin color segmentation